How LLMs Really Work: Daily Users’ Guide

Most articles about LLMs fall into one of two traps:

They’re written for execs and marketers, full of metaphors about “AI as a super-smart intern,” or they’re written for ML researchers, packed with math and jargon.

This is neither.

This is for technical practitioners, people working in SEO, growth, content ops, or product who already use LLMs daily and now want to understand what’s actually happening behind the curtains.

You’re probably not training your own model. But you are:

• Designing prompts and workflows
• Debugging hallucinations or inconsistencies
• Testing model behavior under constraints
• Automating pipelines with ChatGPT, Claude, or Mistral

If that’s you, the goal of this article is to give you the mental model to reason clearly about what these systems can and can’t do.

This article is rooted in a practical understanding of architecture, behavior, and tradeoffs. It’s the technical grounding that will allow you to stop guessing and put you in the driver’s seat of your interactions with AI.

Predicting the Next Token, Not Understanding

At their core, all LLMs, GPT-4, Claude, Gemini, Mistral, operate on one principle:

They are next-token predictors. They do not know what you’re saying. They don’t really understand the world. They are not performing logic, remembering facts, or accessing knowledge.

They are trying to guess what comes next in a sequence of tokens, based on statistical patterns learned from trillions of words.

That’s it.

What a Model Actually Does

When you type:

"The capital of France is"

…the model doesn’t retrieve a fact or look it up. It doesn’t have a table of countries and capitals. It doesn’t even know what a “capital” is.

What it does have is billions of weighted connections that tell it:

In sequences that look like “The capital of France is ___”, the token “Paris” follows with extremely high probability (say, 91.4%).

• “Lyon” might follow with 2.6%
• “London” with 0.3%
• And so on

So it selects the most likely next token: Paris.

That’s the entire game.

This token-by-token prediction continues one step at a time, no lookahead, no global plan, just local guesses. What’s remarkable is that this one dumb mechanism, scaled up enough, starts to look like language comprehension.

Why This Works

You might think this would result in bland autocomplete text. And at small scale, it does. But with enough parameters, training data, and architectural depth, a kind of emergent behavior kicks in.

Because language encodes logic, structure, culture, and context, learning to predict it also requires the model to internalize a lot of the same statistical relationships that underlie reasoning, storytelling, and decision-making.

So when you ask GPT to “Write a landing page for an AI productivity tool,” it isn’t drawing from a template. It’s:

• Matching your instruction to thousands of similar examples from its training set
• Modeling tone, structure, headline patterns
• Predicting sequences token-by-token that are statistically likely in that context

It’s not planning. It’s not strategizing.

But it feels like it is, because the statistical correlations are that good.

Why This Matters

Once you grasp that LLMs only predict next tokens, a lot of behavior that seems strange suddenly makes sense:

This reframes your expectations.

You stop asking “Why did it lie?” and start asking “What pattern was it following?”

You stop wondering “Why did it forget the brief?” and instead think “Where in the context window did that drop out?”

You stop assuming “It must know this,” and realize: it doesn’t know anything. It just mimics everything.

Tokenization, Embeddings, and the Vector Space

Before an LLM can predict the next token, it needs to know what the current ones are, not as words, but as mathematical representations. That transformation happens in two steps:

1. Tokenization: breaking your input into atomic chunks
2. Embedding: mapping those chunks into high-dimensional numerical vectors

This is not just a pre-processing detail. It affects:

• How your input is interpreted
• How much do you pay for API usage
• How your brand names or hashtags are distorted
• How well does your prompt activate relevant model behaviors

Let’s break it down.

Step 1: Tokenization: The Atomic Units of Meaning

LLMs don’t “see” words. They see tokens – fragments of text that might be whole words, parts of words, or even characters, depending on the model’s tokenizer.

For example:

Tokenizers are built to compress common sequences. That’s why “the” is one token, but “TechNova360” might break into ["Tech", "Nova", "360"] which can mess with semantic integrity.

Why This Matters

Cost: You’re billed per token, not per word.

• A 1,000-word article can easily become 1,300–1,600 tokens depending on phrasing and language.

Context length: Models have a max token window (e.g., 8K, 32K, 100K). If your input + response exceeds that, it gets cut off, often silently.

Semantic breaks: If a key phrase is split across tokens, it may activate the wrong internal patterns or fail to be recognized entirely.

Internationalization: Languages like Chinese or Arabic tend to tokenize into more units per sentence, reducing usable context and increasing costs.

Real-World Impact

Imagine your brand is JetBoost360, a product name you’ve invested heavily in.

If tokenized as ["Jet", "Boost", "360"], the model may treat it as a combination of aviation, energy drinks, and numerical suffixes. It doesn’t see it as a cohesive brand concept unless you’ve explicitly conditioned it to do so through example-driven prompting or fine-tuning.

This is why seemingly minor things like capitalization, hyphenation, or hashtags can radically change how your inputs are interpreted by the model. And why prompt results sometimes change for no apparent reason, the tokenization shifted under your feet.

Step 2: Embeddings: Turning Tokens into Vectors

Once a string is tokenized, each token is mapped to a vector – typically 768 to 4096 floating-point numbers, learned during training.

These vectors aren’t arbitrary. They’re positioned in a semantic space where similar meanings cluster near each other.

Let’s say you have these vectors (simplified):

king → [0.2, -0.5, 0.8, ...]
queen → [0.2, -0.5, 0.7, ...]
man → [0.1, -0.3, 0.6, ...]
woman → [0.1, -0.3, 0.5, ...]

You can literally compute:

king - man + woman ≈ queen

This kind of arithmetic works because the model learned relationships between concepts by adjusting vector weights to improve prediction during training.

Embeddings Define Context

The model doesn’t “know” what a word means. But it knows where that word lives in semantic space, and which tokens it tends to co-occur with.

If your input includes:

“We’re launching a new feature for content strategists that uses semantic clustering…”

…the embeddings for “content,” “strategist,” “semantic,” and “clustering” all pull the model’s attention toward related ideas: NLP, keyword research, information architecture, etc.

It’s not thinking. It’s steering through space.

Brand Embeddings in Practice

Even your brand exists in this space.

Take the word Coca-Cola. Based on training data, it lives near:

So when you ask:

“Write a product launch email for Coca-Cola’s winter campaign.”

…the model implicitly draws from its embedding space:

• Holiday themes
• Red & white color associations
• Warm tone
• Brand-style marketing copy

You didn’t ask for any of that explicitly. It comes from statistical proximity in embedding space, shaped by the data the model was trained on.

This is also why it’s hard to “pivot” a brand with AI, you’re not just changing words; you’re fighting the gravity of its learned vector position.

Embedding Gotchas

• Brand names split into multiple tokens may fracture your vector influence, use clean casing and test tokenization first.
• Hashtags are rarely treated as cohesive units.
  • #BrandNewDay → ["#", "Brand", "New", "Day"] these may not activate intended meanings unless well-known.
• Multilingual inputs? Expect token bloat.
  • “hello” = 1 token, “你好” = 2–3 tokens, “مرحبا” = 3–5.
  • Meaning: your context window shrinks dramatically in non-Latin scripts.
• Prompt injection attacks often exploit token boundary behaviors, slipping in control instructions via unexpected token sequences.

Attention and Layers:How the Model Builds Structure Without Understanding

If tokenization and embeddings define the language the model uses internally, then attention is how it forms relationships, how it figures out which tokens matter, and how they influence each other.

This is the breakthrough that made LLMs possible. And to understand how the model processes prompts, generates responses, or “remembers” context, you have to understand how attention works – and how it’s stacked in layers to build abstract representations of meaning.

The Shift: From Sequence to Relation

Earlier models (like RNNs or LSTMs) read text sequentially, one token at a time, updating their internal state at each step.

Transformers do something different:

They look at the entire sequence at once, and use attention mechanisms to decide which tokens should influence each other and by how much.

This shift is why we can now build models that operate on 32k+ tokens instead of choking at 100.

Attention: The Core Operation

Here’s what happens at a high level inside a single attention head:

1. Each token in the sequence is transformed into three vectors:
   • Query: What am I looking for?
   • Key: What information do I contain?
   • Value: What do I offer to the final representation?
2. For each token, the model calculates a compatibility score between its Query and the Keys of all other tokens in the sequence.
3. These scores determine how much “attention” each token should pay to the others, and the model computes a weighted sum of their Value vectors to generate the token’s new internal state.

This means every token is influenced by every other token in parallel, based on learned relevance.

It’s literally learning:

“When I see X, I should pay more attention to Y than to Z.”

Example: Disambiguating “Bank”

Take the sentence:

“The bank by the river was steep.”

Here’s what attention might do:

• The Query for “bank” looks at all Keys
• High compatibility with “river” (0.91), low with “money” (0.05)
• So “bank” gets updated by information from “river,” “steep,” and other location-related tokens

If you wrote:

“He went to the bank to get a loan.”

The context shifts, now “bank” aligns with “loan,” “money,” and “went.”

No rules. No dictionary. Just learned patterns of co-occurrence and influence across vector space.

Multi-Head Attention

This process doesn’t happen once. It happens in parallel “heads”, each focusing on different types of relationships, syntax, grammar, long-range dependencies, word senses, etc.

In GPT-4, there may be hundreds of heads operating simultaneously across dozens of layers. Each head learns a different aspect of relational structure.

Layer Stacking: From Local Patterns to Abstract Thought

Attention by itself isn’t enough. After each round of attention, the output is passed through a feedforward neural network, then normalized and passed to the next layer.

Modern LLMs repeat this process dozens to hundreds of times, each time refining and abstracting the internal representations.

What the Layers Learn

While not hard-coded, we can observe consistent patterns:

When you feed in a prompt like:

“Draft a 5-point marketing strategy for a fitness tech startup targeting Gen Z.”

Here’s what different layers are doing:

• Early layers figure out where one phrase ends and another begins
• Mid-layers recognize the request for strategy structure
• Deep layers pull in concepts related to Gen Z psychology, fitness trends, competitive landscapes, etc.

All of this happens simultaneously, and all the model is doing is updating vector representations based on trained weights.

There’s no logic engine. No internal world model.

Just distributed representation refinement, step by step.

Why This Matters: Practical Implications of Attention and Layers

Here’s what this architecture means in practice, especially if you’re building or debugging AI-driven systems:

1. Long-Term Dependencies Work (Until They Don’t)

Attention makes it possible for the model to link the first sentence in your prompt to the last but only if it’s within the token window. Go beyond that, and it literally forgets.

This is why it can track narrative threads, referents, or stylistic tone, but suddenly drop them in long documents.

2. Repetition Is a Symptom of Shallow Activation

When the model gets stuck repeating phrases (“Our product is innovative, innovative, innovative…”), it’s often because deeper layers didn’t get activated properly. You didn’t give it enough context to push the response into abstraction.

3. Prompts Steer Through the Network

When you write:

“Act as a veteran growth strategist writing for an enterprise SaaS audience…”

…you’re not issuing a role instruction. You’re injecting vector combinations that activate particular heads and layers. This is why even a short role prefix can change the entire output.

4. You Can’t Force Logic Where It Wasn’t Learned

If a model hasn’t seen a kind of reasoning pattern during training, stacking more layers doesn’t help. You can’t prompt your way into logic that isn’t latent in the network.

5. Jailbreaks Work Because Layers Conflict

When users “jailbreak” models by bypassing restrictions, they’re exploiting the gap between deep capabilities and shallow alignment layers, getting the raw model to surface behaviors the guardrails try to suppress.

The Knowledge Illusion: Why LLMs Hallucinate and Sound Confident Doing It

One of the most jarring things about large language models is how confidently they get things wrong.

Ask:

“What are the Q3 2024 sustainability targets in Nike’s latest report?”

You might get back:

“Nike reduced carbon emissions by 23% and achieved 67% use of recycled materials in Q3 2024.”

Sounds plausible. Looks clean. Reads like it came from a real press release.

But it’s fabricated, entirely.

Why?

Because the model doesn’t know anything. It doesn’t have access to Nike’s reports. It doesn’t have a database of facts. It doesn’t even understand that “Q3 2024” is in the future (or past, depending on when you’re reading this).

What it does have is:

• Exposure to thousands of corporate ESG reports
• Learned patterns about how those reports are structured
• A sense that companies often talk about “carbon emissions” and “recycled materials”
• Statistical priors about typical values (10–30% for emissions, 50–70% for recycled content)

From that, it generates a sequence that looks like the kind of thing a real company might say, because that’s what it was trained to do.

Training Objective: Plausibility, Not Truth

Let’s be exact.

The model’s training objective is to minimize the cross-entropy loss between predicted and actual next tokens over massive datasets.

Translated: it wants to maximize the probability of generating text that looks like its training data.

It’s not trained to:

• Retrieve factual information
• Distinguish between accurate and inaccurate outputs
• Recognize truth

It’s only trying to generate what sounds likely in context, statistically.

And in most cases, plausibility is a great proxy. It gets you:

• Accurate capital cities
• Real-sounding product descriptions
• Coherent tone and grammar

But in edge cases, rare events, specific product features, new data plausibility and truth diverge.

When that happens, the model confidently guesses.

Real-World Example: Product Hallucination

Prompt:

“What features does the TechGadget X500 include?”

Response:

“The TechGadget X500 features a 50MP AI-enhanced camera, 128GB of onboard storage, and voice-activated gesture controls.”

Reality: The product was just launched. These features don’t exist.

But the model has seen thousands of product pages and spec sheets. It knows:

• Cameras are often described by megapixel count
• 128GB is a safe default storage claim
• “AI-enhanced” and “gesture controls” are buzzword-safe filler phrases

It’s not lying. It’s doing exactly what it was trained to do:

Generate the next most likely sequence of tokens based on the patterns it has seen before.

This is why it can hallucinate:

• Fake authors
• Non-existent research papers
• Incorrect code libraries
• Broken URLs
• Entire books that sound real but don’t exist

Why This Gets Dangerous

What makes this behavior tricky is that it often looks indistinguishable from truth. Outputs can be:

• Grammatically perfect
• On-brand in tone
• Consistent with previous messages
• Internally coherent

This is the knowledge illusion, the model behaves as if it knows things, when really it’s just assembling probabilistic echoes of things it has seen before.

For casual use, this illusion is harmless.

For production systems? It can be fatal.

Think:

• Financial advice that’s 80% right, 20% made up
• Code suggestions that compile but fail silently
• Health or legal responses that sound authoritative but cite fake sources

Once you trust the model’s tone, you’re already vulnerable.

Why Retrieval Doesn’t Fix It (By Default)

A common countermeasure is to use “grounding”, injecting real data into the prompt so the model has something factual to work with.

Example:

Without grounding: “Summarize our new product features.”

With grounding: “Summarize these product features,” + [actual spec sheet]

That helps. But it only works if:

• The information is explicitly included in the prompt or context window
• The model is incentivized (or instructed) to quote it directly
• Your retrieval system pulls the right documents

And even then, the model might still blend in hallucinated filler unless you’re very careful.

More on that in the RAG section later.

How to Work Around It

Knowing this behavior is structural, not accidental, changes how you use LLMs. You don’t “fix” hallucinations. You design around it.

Tactics that work:

• Don’t ask for data the model can’t possibly have (e.g., internal metrics, recent events)
• Provide concrete facts in the prompt and ask the model to rephrase or organize, not invent
• Use model outputs as drafts or outlines, never final, fact-critical content
• Combine LLMs with external sources via RAG, plugins, or post-processing
• When in doubt: assume it’s making it up, then verify

Also: Prompt specificity matters.

Instead of:

“What are the latest features of TechGadget X500?”

Try:

“Based on the following product description, summarize the main features of the TechGadget X500 in bullet points.”

[Paste actual product description]

This keeps the model in pattern-matching mode, rather than invention mode.

From Pretraining to ChatGPT: How Alignment Actually Works

At this point, we’ve covered how LLMs process inputs, build structure, and generate outputs. But none of that explains what it’s like to use ChatGPT, Claude, or Gemini today.

Those models don’t just autocomplete text.

They:

• Follow instructions
• Refuse inappropriate requests
• Write in a helpful, polite tone
• Ask clarifying questions
• Appear to have a “personality”

So where does that behavior come from?

It’s not baked into the base model.

It’s layered on top through a multi-stage process called alignment.

This section breaks down how a raw model becomes something usable, and what’s really happening when you give a model an instruction like:

“Summarize this in 3 bullet points with a professional tone.”

Stage 1: Pretraining: Learning to Predict Text

This is the foundational stage. The model is trained to do one thing only:

Given N tokens, predict token N+1

Training data is typically huge:

• Web text
• Books
• Forums
• Code
• Wikipedia
• PDFs
• News articles
• Reddit, Stack Overflow, etc.

It doesn’t filter for truth or quality. It’s about diversity of patterns.

By training on trillions of tokens this way, the model becomes a general-purpose text prediction machine, capable of completing any kind of language sequence it has seen before.

But the raw output of a pretrained model is weirdly chaotic. It might:

• Ignore your instructions
• Derail mid-sentence
• Repeat itself
• Write in forum voice
• Complete prompts literally without being helpful

It’s a mimic, not an assistant.

Stage 2: Supervised Fine-Tuning (SFT)

To move the model toward being usable, researchers take the pretrained model and fine-tune it on a curated dataset of input-output examples.

Think:

• “Write a product description for this gadget” → actual good product description
• “Explain quantum computing to a 12-year-old” → clear, simplified answer
• “Translate this from English to French” → correct, formal French

This helps the model learn how humans prefer it to behave, it tunes the model on helpfulness, formatting, tone, and structure.

After SFT, the model behaves more like a tool. It starts following instructions, using bullet points, responding more cleanly.

But it’s still not robust. It might:

• Give inconsistent results
• Sometimes follow your prompt, sometimes ignore it
• Revert to pretraining-style completions mid-way through

It doesn’t yet understand preference. It’s just learned some common task formats.

Stage 3: Reinforcement Learning from Human Feedback (RLHF)

This is the magic layer, the one that makes ChatGPT feel like a product.

Here’s how RLHF works:

1. The SFT model is prompted with a task.
2. It generates several responses.
3. Human raters rank the responses from best to worst.
4. A separate model (the reward model) is trained to predict these rankings.
5. The LLM is fine-tuned to maximize the reward model’s score, using reinforcement learning (typically PPO).

This is how the model learns human preferences:

Not through direct supervision, but by learning to optimize for what humans prefer.

That’s why you now get:

• Polite, safe, helpful tone
• Structured outputs that match what users expect
• Refusals when asked to do unethical or risky tasks
• Clarifying questions when your prompt is ambiguous

This is alignment in practice, the model is no longer just predicting text. It’s predicting text that maximizes reward according to what humans liked in past examples.

Alternative: DPO (Direct Preference Optimization)

RLHF is effective, but complex.

A newer approach, DPO, skips the reinforcement learning loop entirely. Instead, it compares preferred vs non-preferred outputs and directly optimizes the model to prefer the better ones.

It’s faster, simpler, and more stable, and now used in many modern models (e.g., OpenChat, Mixtral, Claude 3, GPT-4 variants).

The result is similar: a model that behaves like a helpful assistant, not just a text generator.

Why This Matters: Alignment Tradeoffs

Alignment isn’t free. It comes with design decisions and consequences.

The more alignment you apply, the more the model behaves like a tool.

But you also lose some of the wildness that made early models surprising.

Practical Impacts

• ChatGPT vs GPT-4 API: Same architecture, different alignment layers = noticeably different behavior.
• Base models (like raw LLaMA): Ultra-flexible but need prompt babysitting.
• Highly aligned models (like Claude 3 Opus): Predictable, safe, sometimes too eager to say “I’m sorry, I can’t do that.”

You’ll see this in how models respond to:

• Edge-case instructions
• Creative prompts
• Slightly risky or ambiguous tasks

This Is Why Jailbreaks Work

Every alignment layer is a set of learned preferences, not hardcoded rules.

When users “jailbreak” a model, they’re trying to:

• Suppress the alignment signal
• Trigger deeper latent capabilities from the base model
• Access behavior the preference layers would normally block

That’s why techniques like DAN (Do Anything Now) worked in early ChatGPT, they used prompt engineering to convince the model to ignore its reward incentives.

Alignment isn’t a firewall. It’s a probabilistic overlay.

Context Limits, Token Budgets, and Latency Bottlenecks

Once you understand how LLMs are built and trained, a new question emerges:

• “Why does the model sometimes forget what I just said?”
• “Why does it ignore my prompt halfway through a response?”
• “Why is it so slow today?”
• “Why did this cost $300?”

Welcome to the world of operational constraints, the hard ceilings that define how LLMs behave in production.

This section breaks down:

• What LLMs can “remember” (and why they forget)
• How token usage affects both cost and behavior
• What actually determines speed and latency
• Where things break when you push too far

Context Window: The Model’s Working Memory

LLMs don’t have memory. They have context.

Everything the model “knows” in a given interaction has to fit into a fixed-length token buffer, called the context window. This includes:

• Your prompt
• Any examples you provide
• Instructions and formatting
• System messages (in chat interfaces)
• The model’s own output (as it generates it)

Once that window fills up, earlier content is cut off. It’s gone, not in latent memory, not “deprioritized,” just literally inaccessible.

Context Limits by Model

When you hear “long context,” this is what’s being discussed.

Practical Failure Modes

• Prompt bloat: Long instructions or example chains crowd out room for the model’s output
• Forgotten details: Earlier messages or background info disappear if they fall off the edge
• Sudden incoherence: The model starts repeating, hallucinating, or changing tone mid-output, often a sign it ran out of room
• Agent breakdown: Multi-step processes lose their chain of logic once intermediate states scroll off the context window

This is why you often need to re-inject key information at each turn especially for system prompts, brand tone, or formatting rules.

Token Budget: You Pay for What You Stuff

You’re charged per token, both for input and output. That includes:

• Every word in your prompt
• System prompts
• History (in chat interfaces)
• The model’s generated response

Example Breakdown

Let’s say you:

• Send a 2,000-word prompt (~2,700 tokens)
• Get a 1,200-word response (~1,600 tokens)
• That’s 4,300 tokens total.

Now imagine doing this 500 times a day in a workflow or app. Your infra bill can scale fast, even before RAG or orchestration overheads kick in.

Optimization Tips

• Prune redundant prompt components
• Use prompt compression or summarization to retain info in fewer tokens
• Split long documents into hierarchical chunks
• Use smaller models (GPT-3.5, Claude Haiku) where quality allows
• Fine-tune for your task instead of repeating long instructions every time

Latency: Why It’s (Sometimes) So Damn Slow

There are two components to LLM response time:

First-token latency

• Time from sending request → model starts responding
• Often 0.5s to 2s, depending on model, queue load, and infra

Streaming speed

• How fast it generates text after the first token
• ~20–100 tokens/second depending on model, load, output length, and your client interface

So a 1,000-word answer (~1,300 tokens) might take 10–30 seconds end to end.

What Affects Speed?

If you’re building real-time interfaces (e.g., search, chatbots), latency becomes a core UX issue. It’s why OpenAI throttles GPT-4 throughput unless you’re on their higher-cost Pro or Team plans.

Rate Limits and Concurrency

API rate limits govern how many requests you can send per minute, and how many tokens you can process.

Example limits:

Exceed these and you’ll:

• Hit 429 errors
• Have jobs queue or fail
• Need queuing logic, backoff retries, or distributed orchestration

For any serious production workflow, you need retry handling, batching, and token accounting, or you’ll hit walls fast.

What You Can Do About It

To operate efficiently and avoid silent failures:

Plan for Token Constraints

• Summarize chat history before appending
• Avoid repeating system prompts verbatim every turn
• Chunk long docs into manageable pieces and summarize context

Monitor Cost Proactively

• Use token estimators before sending
• Log input/output separately
• Don’t blindly pass through large context, it adds up fast

Control Latency Where It Matters

• Stream responses where possible
• Pre-cache static LLM responses when deterministic
• Use GPT-3.5 or Claude Haiku for fast, shallow tasks
• Avoid using GPT-4 for trivial completions

Sampling Parameters: Steering Output with Temperature, Top-p, and Top-k

Once a model has processed your input and built its internal representation, it’s ready to generate output, token by token. But here’s the twist:

At each step, the model doesn’t pick a word. It creates a probability distribution over all possible tokens. Then it samples.

In other words, the model is saying:

“Given everything I know, here’s a list of next-token candidates, and how likely each one is.”

How you control which token it picks from that distribution is where sampling parameters come in. These are your steering wheel, gas pedal, and traction control.

Get them wrong, and your outputs will be flat, repetitive, or incoherent.

Get them right, and you unlock tailored tone, variation, and creativity with precision.

The Core Distribution

Imagine your prompt ends with:

“Our new product is…”

The model might produce a distribution like this (simplified):

There are thousands of candidates, but most have near-zero probability. The model needs to pick one.

Which one it picks depends on how you sample from this list.

Temperature: The Randomness Dial

Temperature controls how “sharp” or “flat” the probability distribution is before sampling.

• Temperature = 0.0: Always pick the most likely token.
  • → Output is deterministic and repetitive.
• Temperature = 1.0: Raw sampling, pick tokens based on original probabilities.
  • → Output is natural, with some variety.
• Temperature > 1.0: Flatten the distribution, make lower-probability tokens more likely.
  • → Output becomes more creative, surprising, or chaotic.

• Use low temperature for structured tasks (e.g. legal, finance).
• Use high temperature for brainstorming, fiction, or ideation.

But temperature alone isn’t enough. It controls the shape of the distribution, but not which parts you allow the model to pick from.

Top-p, The Probability Cutoff

Top-p (aka nucleus sampling) controls how many tokens the model can choose from by cumulative probability mass.

Let’s say:

top-p = 0.9

That means:

“Only sample from the smallest set of tokens whose combined probability is ≥ 90%.”

So if:

• “innovative” = 16%
• “designed” = 13%
• “powerful” = 10%
• “available” = 8%
• “revolutionary” = 6%
• “easy” = 4%
• …and so on…

The model adds up:

16 + 13 + 10 + 8 + 6 + 4 = 57%

…still not enough. It keeps going until it hits 90%. Then it samples only from those tokens.

Why It Matters

Top-p lets you control how much of the long tail the model can access.

• Low top-p (0.5–0.7): Focus on most likely tokens → safe, repetitive language
• High top-p (0.9–0.95): Allow more creative variety
• Very high top-p (0.99+): Include rare, surprising tokens → risk of weird output

It’s often better than temperature for controlling diversity while maintaining coherence.

Top-k, The Hard Cutoff

Top-k is simpler:

“Only sample from the top k most likely tokens.”

If k = 5, the model only considers the 5 most likely next tokens,  regardless of how much probability they cover.

This is more rigid than top-p, but still useful in models that support it (like some open-source ones).

Combined Use: The Interaction Effect

In most APIs (OpenAI, Anthropic, Cohere), temperature and top-p are used together.

You scale the distribution with temperature, then truncate it with top-p.

This gives you nuanced control:

• Low temp + low top-p → robotic precision
• Medium temp + medium top-p → clean, varied copy
• High temp + high top-p → weird, creative ideas
• High temp + low top-p → incoherent or unstable
• Low temp + high top-p → safe but sometimes flat

Don’t blindly crank the temperature. Start with top-p = 0.9 and adjust only when you need more variety or more constraint.

Real-World Tuning Examples

You can run A/B tests on these settings. In some pipelines, teams run multiple completions at different temperature/top-p settings and pick the best, or ensemble them.

Pro Tip: When Output Is Boring, Don’t Just Raise Temperature

Many people try to fix boring output by adjusting the temperature from 0.7 → 1.5. But this often results in nonsense, not creativity.

Instead:

• Keep temp in 0.7–1.0 range
• Increase top-p from 0.9 → 0.95
• Or use multiple completions at same temp and filter them

It’s like tuning a guitar: small changes matter more than you think.

Emergence: The Capabilities We Didn’t Train For (But Got Anyway)

By now, the core architecture of LLMs should be clear:

• Predict the next token
• Use attention to build context
• Learn relationships through training
• Shape behavior with alignment and sampling

It’s a statistical system. A powerful one, but still just math.

And yet…

• GPT-4 can solve logic puzzles that smaller models fail.
• Claude 3 can follow multi-step reasoning over 50,000 words.
• Even older models can do arithmetic, write coherent stories, or answer complex strategy questions.

No one explicitly taught the model how to do those things.

This is the emergence problem:

Certain capabilities, reasoning, abstraction, even theory of mind, don’t appear until models reach a certain scale, and then they show up suddenly.

Not gradually. Not linearly. Abruptly.

What Is Emergent Behavior?

In LLMs, emergence refers to qualitative shifts in ability that arise from quantitative increases in model size, data, or training steps.

For example:

These aren’t tricks. They’re unintended capabilities, side effects of scaling a prediction engine beyond certain thresholds.

Example: Chain-of-Thought Reasoning

Try this:

“A farmer has 4 sheep and 3 cows. Each sheep gives 2 gallons of milk per day, and each cow gives 5. How much milk total?”

Smaller models fail. They output something like:

“The total is 9.”

Larger models, when prompted correctly, can do:

“4 sheep × 2 = 8 gallons

3 cows × 5 = 15 gallons

Total = 8 + 15 = 23 gallons”

That’s not memorized. That’s multi-step reasoning, across multiple concepts.

No one explicitly taught it arithmetic. It learned this behavior by seeing many examples of similar logical reasoning in its training data, and discovering that modeling latent structure improves next-token prediction.

So What’s Actually Going On?

The leading hypothesis is:

As models scale, they begin to build internal latent representations of the world, not because we asked them to, but because it helps reduce loss.

In other words:

To predict text well, the model needs to implicitly model the structure of the world that generates that text.

So it begins to:

• Infer causality
• Track actors, beliefs, timelines
• Represent beliefs about beliefs (theory of mind)
• Simulate the structure of logic, tools, functions, and arguments

But it does this implicitly, not via a symbolic reasoning engine, but as emergent statistical behavior in a dense neural network.

This is both powerful and dangerous:

• Powerful: Because the capabilities are real
• Dangerous: Because we don’t understand how or why they happen

Why This Matters

You’re not just working with a pattern matcher anymore. You’re working with a statistical simulator of possible worlds, one that can:

• Infer intent from subtle cues
• Generate reasoning steps it’s never seen before
• Role-play different agents with different perspectives
• Solve multi-hop research questions
• Produce genuine novelty under certain sampling conditions

That’s why LLMs can:

• Write original poems in the style of obscure poets
• Simulate APIs that don’t exist
• Play both sides of a negotiation
• Generate code for libraries it’s never seen, and get it mostly right

You didn’t train it to do those things. It absorbed them from the structure of the data, and modeled them internally to get better at text prediction.

Open Questions in Emergence

We don’t fully understand emergent behavior, even at the research frontier. Major open questions include:

• Why do certain abilities emerge at specific scale thresholds?
• Can emergence be reliably predicted or forced?
• How much of “reasoning” is real, and how much is just imitation?
• Do models represent abstract ideas like goals, beliefs, or planning, and if so, how?
• Could dangerous behaviors (e.g., manipulation, lying) also emerge without us realizing it?

This is not a hypothetical concern.

OpenAI, Anthropic, DeepMind, and others actively research model interpretability and safety because emergence makes capabilities hard to anticipate or constrain.

Grounding and RAG: Connecting LLMs to Reality

By now, it should be clear that language models operate in a kind of statistical dreamworld – one built from trillions of words, but disconnected from current facts, structured data, or real-time context.

They generate text that sounds true.

But they don’t know anything is true.

So how do you make a model that can:

• Pull from your live product database?
• Answer questions based on a PDF or Notion doc?
• Generate copy using up-to-date performance stats?
• Stay on-brand, on-message, and in-bounds?

Enter: RAG: Retrieval-Augmented Generation.

What RAG Actually Is

RAG is a design pattern, not a model. It combines:

1. A retriever that pulls relevant documents or facts from an external source (e.g. search index, database, vector store)
2. A generator (your LLM) that conditions its output on those documents

In short:

“First find the right info. Then feed it to the model. Then ask your question.”

Instead of prompting the model from scratch, you augment the prompt with retrieved, factual content.

This turns the model into a kind of statistical interface for structured information, letting it generate fluent, human-readable responses grounded in actual source material.

Basic RAG Pipeline

Here’s a simplified workflow:

1. User prompt: “What are the key features of the new ACME Q3 software release?”
2. Retriever: Searches your internal docs, changelogs, or CMS → Finds: Release notes, support articles, product brief
3. Context builder: Packages relevant chunks into the prompt
4. LLM: Generates response like:
   

   “The ACME Q3 release includes a revamped dashboard, improved mobile sync, and enhanced multi-user permissions…”

No hallucination. No guesswork. It only knows what it was shown.

This is how many modern AI features work:

• Notion AI
• Perplexity.ai
• Custom GPTs with uploaded files
• Enterprise copilots pulling from private knowledge bases

Why It Works

LLMs are incredible text generators, but terrible knowledge stores.

What they can do reliably:

• Parse unstructured text
• Summarize, rephrase, extract
• Answer questions from provided content
• Follow style and tone instructions

What they can’t do:

• Retrieve accurate facts from long-term memory
• Know what’s current or canonical
• Stay updated past their training cut-off
• Access your internal data (unless you give it)

RAG fixes this by injecting the relevant facts at generation time.

You don’t retrain the model. You give it better ingredients.

Practical Use Cases

SEO or Content

Prompt:

“Based on this blog post and these SERP results, write a new version optimized for this keyword cluster.”

RAG provides:

• Source blog post
• Competitor content
• Keyword intent data

LLM:

• Outputs optimized copy that reflects real competitive context

Internal Knowledge Base

Prompt:

“What’s our refund policy for EU-based customers who paid in crypto?”

RAG provides:

• Snippets from your TOS
• Support doc on refunds
• Payment policy exceptions

LLM:

• Generates a precise support response, grounded in actual policy

Analytics and Reports

Prompt:

“Summarize Q4 performance from our internal dashboard exports.”

RAG provides:

• CSV/JSON metrics
• Commentary from analyst briefs
• Weekly reports

LLM:

• Writes a human-readable executive summary based on real data

RAG is Architecture, Not Prompting

You can’t “ask better questions” to get grounded answers from a base LLM.

You need:

1. Document ingestion (PDFs, websites, markdown, Notion, etc.)
2. Text chunking (break long docs into sections)
3. Vector embeddings (map each chunk into semantic space)
4. Similarity search (retrieve relevant chunks for a given query)
5. Prompt templating (combine question + chunks + instructions)
6. Context management (fit it all into the token window)

This is what tools like LangChain, LlamaIndex, Haystack, or custom vector search pipelines do under the hood.

When RAG Goes Wrong

RAG is powerful, but brittle if implemented poorly.

Common failure points:

Fixes involve:

• Better document segmentation (semantic chunking, not just by paragraph)
• Tuning retrieval quality (top-k, hybrid search)
• Source filtering (metadata, tags, time filters)
• Prompt scaffolding (clear instructions on which source to trust)

Bonus: RAG Enables Privacy + Control

You don’t need to fine-tune or retrain a model to specialize it.

You don’t need to upload proprietary data to OpenAI.

RAG lets you:

• Keep internal data local
• Change knowledge bases without retraining
• Inject personalized or private info at runtime
• Update the system instantly when facts change

This is how enterprise AI copilots stay accurate, and how open-source LLMs become task-specific without extra training.

Conclusion

Understanding LLMs isn’t about memorizing transformer architectures or debugging attention heads. It’s about building the right mental model, one that lets you reason clearly about what these systems can and can’t do.

The key insights:

• LLMs are statistical pattern matchers, not knowledge bases
• They predict plausible text, not truth
• Alignment makes them useful, but doesn’t change their fundamental nature
• RAG grounds them in reality when you need factual accuracy
• Operational constraints (tokens, latency, cost) shape how they behave in production

With this foundation, you can stop guessing and start engineering your AI workflows with confidence.

The future of AI isn’t about building smarter models, it’s about building smarter systems that leverage what these models are actually good at.

Notes:

Vaswani, Ashish, et al. “Attention is All You Need.” NIPS 2017 – https://arxiv.org/abs/1706.03762

Brown, Tom, et al. “Language Models are Few-Shot Learners.” NeurIPS 2020. – https://arxiv.org/abs/2005.14165

Petroni, Fabio, et al. “Language Models as Knowledge Bases?” EMNLP 2019. – https://arxiv.org/abs/1909.01066

Wei, Jason, et al. “Chain of Thought Prompting Elicits Reasoning in Large Language Models.” NeurIPS 2022 – https://arxiv.org/abs/2201.11903

Olah, Chris, et al. “The Building Blocks of Interpretability.” Distill 2018 – https://distill.pub/2018/building-blocks/

Henderson, Peter, et al. “Aligning AI With Shared Human Values.” arXiv 2023-https://arxiv.org/abs/2008.02275

Merrill, William, Petty, Jackson, & Sabharwal, Ashish. “The Illusion of State in State-Space Models.” ICML 2024 – https://arxiv.org/abs/2404.08819

Guo, Yufei, et al. “Bias in Large Language Models: Origin, Evaluation, and Mitigation.” arXiv 2024- https://arxiv.org/abs/2411.10915

Zhang, Xiao, et al. “Co-occurrence is not Factual Association in Language Models.” NeurIPS 2024- https://arxiv.org/abs/2409.14057

Banerjee, Sourav, et al. “LLMs Will Always Hallucinate…” arXiv 2024 – https://arxiv.org/abs/2409.05746

Liu, Iris. “RAG Hallucination: What is It and How to Avoid It.” K2View blog, April 2025.- https://www.k2view.com/blog/rag-hallucination/Hao, Shibo; et al. “Reasoning with Language Model is Planning with World Model.” arXiv 2023 https://arxiv.org/abs/2305.14992