Large Language Models Explained
A large language model predicts tokens in sequence. Scale, diverse training data and transformer networks allow this simple training objective to produce remarkably flexible language capabilities.
Learning objectives
What you will be able to do
- Explain large language models explained accurately in your own words.
- Recognise how the concept appears in real AI products and professional work.
- Identify an important limitation, risk or evaluation requirement.
- Apply the concept through a practical activity and knowledge check.
Core explanation
Build the right mental model
During pretraining, the model repeatedly predicts missing or next tokens across very large text and code collections. The learned parameters capture statistical relationships among words, concepts, styles and structures.
Instruction tuning and preference-based methods make the pretrained model more useful in conversation. Retrieval, tools and system instructions can further adapt behaviour without changing all model parameters.
An LLM has a knowledge cutoff, finite context and no automatic guarantee of truth. It may combine patterns into a convincing but unsupported claim. Use retrieval and verification for consequential work.
Follow the path from text to a response
A common generative language-model pipeline begins by converting input text into token identifiers. The model processes those identifiers and produces scores over possible next tokens. A selection procedure chooses a token, appends it to the sequence and repeats. The final string can look like a coherent explanation even though no separate fact checker is guaranteed to have checked every claim. Tools and retrieval may be added by the surrounding application, but they are not implied by a conversational interface.
Pretraining and use are different stages. Training changes model parameters from many examples; inference uses parameters to process a particular request. Providing a document during a conversation usually supplies context for that request rather than retraining the entire model on the document. A product may separately retain data or use it for training according to its settings, so the computational distinction is not a privacy promise.
Understand what instructions can and cannot change
A request such as “answer in three bullets” shapes the desired output. A relevant example can clarify the format. A supplied source passage can provide facts unavailable in the prompt alone. None of these guarantees that the model will obey perfectly. Very long inputs, conflicting instructions or unfamiliar subject matter can still produce an unsuitable response.
Instruction tuning and other post-training methods can improve behaviour, but a helpful style does not certify every answer. An assistant might apologise confidently for a correct answer or defend an incorrect one. Evaluate the actual claim and evidence rather than the confidence, friendliness or apparent reasoning sophistication. A convincing explanation of an invented command remains an invented command.
Use uncertainty as a workflow signal
When a fact is missing from the supplied material, a useful response identifies the gap. This is especially important for version-specific software instructions, local policies and recent events. Ask the model to separate supported statements from assumptions, then independently check the statements that would affect an action.
A compact response contract is: “Use only this source, quote the relevant clause, explain it in plain language, and say when the source does not answer the question.” Quoting a passage can make checking easier, but quotations themselves can be inaccurate. Compare them with the original. Do not treat the ability to produce a source-looking sentence as proof that the source was consulted.
Pretraining
Broad learning from large datasets using a prediction objective.
Instruction tuning
Additional training that improves response to user instructions.
Inference
Using the trained model to generate an output.
Hallucination
A plausible output not adequately supported by facts or provided evidence.
Worked example: an answer whose decisive fact is missing
Fictional help-desk source: “The training server restarts each Sunday at 06:00. Users should save open work before the restart. Planned downtime is 15 minutes.” Question: “Will an unsaved editor buffer be recovered automatically after the restart?” The source says to save work but does not specify automatic recovery.
An unsuitable answer is: “Yes, the server automatically restores all unsaved buffers.” This adds a guarantee absent from the source. A suitable answer is: “The notice does not state whether unsaved buffers are recoverable. It tells users to save work before the Sunday restart. Check the editor’s recovery behaviour and the server’s restart procedure with the administrator.”
The better answer distinguishes what the notice supports from what remains unknown. It does not need to guess an operating system or invent a recovery path. If a retrieval tool later obtains approved editor documentation, that can support a more specific response, but the added source must actually address the recovery scenario.
Notice that “save before 06:00” and “downtime is planned to be 15 minutes” are separate claims. A plan is not a guarantee that service always returns at 06:15. Preserve that qualification when translating the notice into advice.
Apply your learning
Practical activity
- Spend 4 minutes writing a fictional notice with three explicit facts and one deliberately missing detail.
- Spend 5 minutes drafting three questions: one directly answered, one requiring arithmetic, and one unanswerable from the notice.
- Spend 7 minutes using the response contract or drafting model answers yourself. Highlight where an answer uses a fact, an inference or an assumption.
- Spend 4 minutes rewriting any answer that overstates certainty and recording the evidence needed to resolve the missing detail.
Compare with an explained example answer
For the restart notice, “Which day?” is directly answered: Sunday. “What is the planned restart window?” can be derived as 06:00–06:15, subject to the stated plan. “Are unsaved buffers recovered?” remains unanswered.
An acceptable review labels the first as source-supported, the second as a simple time calculation with a qualification, and the third as unknown. Do not manufacture an answer simply to complete every row. The skill is distinguishing evidence levels, not obtaining a confident sentence for every question.
Write your answers in a learning journal. The value comes from connecting the concept to your own profession.
Knowledge check
Test your understanding
Q1Does adding a document to a prompt automatically retrain the model?
Answer: Normally it provides inference-time context. Training changes parameters through a separate process; a service’s retention and training policies still require checking.
Q2Why can a model produce a correct-looking unsupported answer?
Answer: A plausible continuation can fit language patterns without being grounded in the needed evidence. The application must supply and check evidence where required.
Q3What should an assistant do when the source omits a decisive condition?
Answer: Identify the omission, answer only the supported part and state which additional evidence would resolve the question.
If you cannot explain the answer without reading it, revisit the core explanation and example before continuing.
Continue with authoritative sources
Important industry resources
These links lead to official organisations, industry laboratories or established open-source learning projects.