Text Generation and Language Models¶

What This Is¶

This page is about deciding when free-form generation is the right tool and how to use it without fooling yourself. The first question is usually not temperature or beam width. The first question is:

do I actually need generation, or would classification, retrieval, reranking, or template selection be safer

Generation is powerful, but it is also the easiest way to produce fluent nonsense.

When You Use It¶

summarization
answer drafting
code completion
open-ended explanation or rewriting tasks
constrained generation where the output itself must be text

Start With The Task Decision¶

Choose the smallest workflow that fits the task:

Task shape	Better first move	Why
fixed label or category	classifier or reranker	you need a decision, not open text
answer from trusted documents	retrieval plus grounded answer	facts matter more than surface fluency
fixed schema extraction	constrained output or parser	format stability matters more than creativity
explanation, draft, or rewrite	generation	the output itself must be language

Many teams reach for generation too early. That usually creates evaluation problems, not better products.

The Generation Loop¶

Use the same loop every time:

define the output contract
choose a conservative decoding rule
inspect a small batch of outputs
verify facts, format, and refusal behavior
decide whether prompting is enough or whether you need stronger grounding or tuning

If you skip the output contract, debugging becomes much harder.

Minimal Pattern¶

This is the shortest useful generation pattern:

generated = model.generate(
    **inputs,
    max_new_tokens=120,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)

Those knobs matter only after the task is defined clearly.

Decoding Rules¶

Use decoding to match the task, not to chase novelty:

Setting	Good for	Risk
greedy	deterministic formatting, simple completions	repetitive or brittle outputs
lower temperature	factual or constrained tasks	can become too rigid
higher temperature	brainstorming or creative drafting	higher hallucination risk
top-p sampling	balanced open generation	still needs verification

For factual tasks, start conservative. For creative tasks, loosen the sampling only after the format is stable.

Prompt Ladder¶

Move up this ladder only when the lower rung clearly fails:

clear instruction with explicit output format
few-shot examples if the format is unstable
retrieval or grounding if facts matter
fine-tuning only when you have enough examples and the prompt still cannot produce stable behavior

That keeps the workflow honest. Fine-tuning is not a substitute for a vague task definition.

What To Inspect First¶

Do not judge generation only by how smooth it sounds. Inspect:

whether the output follows the requested format
whether the answer is grounded or invented
whether repetition or drift appears after a few sentences
whether a simpler non-generative workflow would be safer
whether the evaluation matches the task

If factual accuracy matters, manual spot checks and grounding checks matter more than fluency.

Failure Pattern¶

The biggest failure is treating generated text as if fluency proved correctness.

That shows up as:

polished hallucinations
vague answers that sound complete
brittle prompt behavior hidden by a few cherry-picked examples
evaluation with only BLEU, ROUGE, or "it looks fine"

Common Mistakes¶

using generation for tasks that are really classification or ranking
setting temperature too high on factual tasks
blaming the model for an ambiguous prompt with no output contract
evaluating only with surface metrics
fine-tuning on too little domain data and calling the result specialization
forgetting to test failure cases, refusals, and malformed outputs

A Good Generation Note¶

After one run, the learner should be able to say:

why generation was chosen over a simpler workflow
what the output contract was
which decoding rule was used and why
what failure pattern appeared first
what evidence would justify grounding, retrieval, or fine-tuning

Practice¶

Turn one generation task into a more constrained output contract.
Compare greedy decoding against a moderate sampling setup.
Show one example where retrieval would be safer than pure generation.
Explain when prompting is enough and when fine-tuning becomes reasonable.
Name one failure case where the output sounds good but is still wrong.

Runnable Example¶

Longer Connection¶

Continue with Attention and Transformers for the architecture behind modern language models, Text Representations and Order for the simpler text baselines that should come first, and Text Workflows Beyond Classification for fuller retrieval and generation-style workflows.