Introduction to LLMs
Introduction to Large Language Models: understand how LLMs work, their capabilities, and applications in modern AI systems.
What is a large language model?
A large language model (LLM) is a neural network trained to predict tokens in language. By repeating that prediction one token at a time, it can draft text, summarize documents, answer questions, transform structured data, and help call software tools.
In plain language, an LLM is a powerful pattern generator. It can produce a fluent answer without possessing an independent source of truth, so useful systems surround it with context, evidence, validation, and clear authority boundaries.
The invariant to remember
An LLM generates a likely continuation from its model weights and retained context. A likely continuation is not automatically a true claim or an authorized action.
Tokens
Text units
Both input and generated output consume the request budget.
Parameters
Learned weights
Training adjusts numeric values that encode reusable language patterns.
Context
Visible request state
Only retained instructions, evidence, history, and tool results can directly condition a response.
Evidence
Release boundary
External checks determine whether an answer should be used, reviewed, or blocked.
Learn the four layers of an LLM system
An LLM product is more than one model checkpoint. Keeping its layers separate makes capabilities, state, cost, and failures easier to reason about.
Text becomes IDs
Tokenizer
A tokenizer maps text into vocabulary units. A token may be a word, part of a word, punctuation, or bytes; token count is therefore not the same as word count.
Learned patterns
Model weights
Parameters are learned numeric weights. They store statistical behavior from training, not an inspectable database of verified facts.
Request-time memory
Context window
The context contains the tokens retained for one request. Material omitted or truncated from that window cannot be directly attended to.
One step at a time
Decoder
The decoder turns model scores into a next-token choice. Temperature and sampling change selection behavior; they do not add knowledge.
Trace how one response is generated
Autoregressive generation repeatedly predicts one token, appends it to the visible sequence, and predicts again. Every chosen token becomes context for the next step.
1 Represent
Tokenize the request
Convert instructions, user text, evidence, and prior turns into token IDs under one versioned tokenizer.
2 Attend
Build contextual states
Transformer layers let each position combine relevant information from the visible prefix while respecting the model's attention rules.
3 Predict
Score the vocabulary
The model emits one score, or logit, for each possible next token. Softmax converts the scores into a probability distribution.
4 Decode
Choose and repeat
Greedy decoding selects the highest probability; sampling may select another allowed token. The result is appended and the cycle continues.
Probability answers "What continuation fits this model and context?" It does not answer "Is this claim true in the external world?"
Separate training from inference
Training changes model weights; inference uses a fixed model bundle to produce an output. Mixing these phases leads to false assumptions about memory, freshness, and cost.
From source data to a governed response
Each boundary creates a different artifact, owner, and failure mode.
Training data
Curated corpus
Collect, filter, deduplicate, document, and permission the material used for optimization.
Weight updates
Training run
Minimize prediction loss across batches, checkpoints, and distributed workers while recording data and code lineage.
Release artifact
Versioned model bundle
Pin checkpoint, tokenizer, adapter, precision, runtime, and policy versions as one deployable unit.
Runtime context
Inference request
Combine current instructions, authorized evidence, history, and tool contracts without changing the base weights.
Product decision
Validated result
Check support, format, policy, and authority before releasing text or permitting a side effect.
Estimate training work without mistaking it for a quote
For a dense decoder, a common planning approximation is 6 x N x D floating-point operations, where N is parameter count and D is training-token count. A 7-billion-parameter model trained on 300 billion tokens is therefore about 12.6 x 10^21 operations before hardware utilization, communication, failed work, evaluation, and data processing.
- More parameters increase learned capacity and weight memory.
- More useful training tokens provide more optimization examples.
- Hardware peak throughput is not sustained end-to-end throughput.
- A lower training loss does not prove factuality, safety, or product usefulness.
Design the request path around explicit boundaries
A production request needs owners for identity, evidence, compute, validation, and action. The model server should not silently absorb all five responsibilities.
A bounded LLM request path
The generated text remains a proposal until downstream controls approve its use.
Identity and budget
Admission gateway
Authenticate, authorize, rate-limit, attach request identity, and reject work that cannot meet its quota or deadline.
Instructions and evidence
Context builder
Select authorized sources, preserve provenance, and fit instructions, history, evidence, and output allowance into the token budget.
Model execution
Inference scheduler
Route a pinned model bundle, queue compatible work, manage live attention state, stream output, and propagate cancellation.
Claims and format
Validators
Check schema, citations, policy, task-specific rules, and uncertainty before a consumer treats generated text as usable.
Authority boundary
Tool or user
Execute only permitted actions. Consequential side effects need bounded tools, idempotency, and human approval where required.
Budget context deliberately
- Reserve space for system instructions and tool contracts before accepting user content.
- Retrieve only relevant, authorized evidence; a larger context window does not make irrelevant material useful.
- Summarize history only when the summary preserves decisions, constraints, and provenance needed later.
- Reserve output tokens explicitly so input cannot consume the entire window.
- Define what happens when evidence, history, or the requested output no longer fits.
Choose a model from the workload contract
A model choice is a systems decision. Begin with the task and eliminate options that violate hard constraints before comparing average benchmark scores.
Fast default
Small general model
Use for bounded extraction, classification, rewriting, and routine chat when representative tests show enough quality. Lower latency and cost often improve the complete product.
Capability headroom
Large general model
Use when difficult reasoning or broad language coverage creates measured task value that justifies higher latency, token cost, and capacity pressure.
Managed runtime
Hosted model
The provider owns serving infrastructure, while you still own data policy, prompt behavior, evaluation, dependency risk, and complete task economics.
Infrastructure control
Self-hosted model
Useful for residency, customization, or offline constraints when the team can own accelerators, batching, memory, scaling, upgrades, and incident response.
Compare the complete contract
- Quality: task success, groundedness, language and domain slices, structured output, and safe refusal behavior.
- Performance: time to first token, output-token cadence, tail latency, concurrency, and cancellation waste.
- Data: retention, residency, training use, deletion, access controls, and license obligations.
- Operations: version pinning, quotas, observability, fallback compatibility, deprecation, and rollback.
- Economics: input, output, retrieval, tools, review, retries, idle capacity, and cost per accepted task.
Choose the smallest deployable bundle that clears the complete contract with measured headroom. Parameter count alone is not a quality guarantee.
Recognize the common failure modes
LLM failures often look polished. Design the surrounding system to detect unsupported, stale, malformed, unsafe, or unauthorized output before it becomes a product action.
- Hallucinated claim: fluent text lacks supporting evidence or contradicts an authoritative source.
- Context truncation: the system drops a critical instruction, decision, or source while preserving less important text.
- Prompt injection: untrusted content attempts to override instructions or obtain tools and data outside its authority.
- Format drift: generated output violates the schema expected by downstream software.
- Tool replay: a timeout and retry repeat a consequential side effect without reconciliation or an idempotency key.
- Model drift: a new checkpoint, tokenizer, adapter, or prompt changes behavior without comparable release evidence.
- Overload: long prompts and outputs exhaust queue, memory, or token budgets and push tail latency beyond the service objective.
Degrade explicitly
- abstain or ask for evidence when a factual claim cannot be supported;
- queue, reject, or route before resource exhaustion instead of silently truncating;
- disable consequential tools when authorization or policy dependencies are unavailable;
- show users when retrieval, validation, or a fallback model changed the response path;
- preserve the previous proven bundle until rollback and replay drills pass.
Operate the complete system, not only the model
Production readiness means proving that the request path can explain, bound, and recover from failures across model, data, tools, and policy.
Keep these mental models
- An LLM predicts continuations; probability is not independent truth evidence.
- Training changes weights, while inference conditions fixed weights with request-time context.
- Tokens are a shared budget for latency, memory, capacity, and cost.
- The context window is finite and request-scoped; it is not durable memory.
- A model output is a proposal until evidence, validation, policy, and authority checks approve its use.
- Model selection should optimize accepted task outcomes, not parameter count or one benchmark.