Natural Language Processing with Large Language Models Cheat Sheet
A large-language-model NLP reference for text cleaning, tokenization, prompt design, embeddings, retrieval, generation evaluation and privacy.
What this reference covers
This NLP reference breaks text work into data preparation, model calls, prompting, embeddings, retrieval, structured generation and evaluation. It is designed to help a developer make a complete input-to-output workflow rather than solve every task with a longer prompt.
Define the task and failure cost
Distinguish classification, extraction, summarization, question answering and generation. State input fields, output schema, refusal conditions and acceptable errors. Preserve source, time, permission and paragraph identifiers when long documents are cleaned or split.
Choose prompting or retrieval deliberately
Simple tasks may use a structured prompt. Knowledge-heavy tasks may add embeddings and retrieval, but retrieved text must actually support the answer. Evaluate recall, citation completeness, factual consistency, stability, cost and sensitive-data exposure.
Maintenance note
Keep minimal logs and access policies for inputs, embeddings, indexes and outputs. Fluent generated text is not a fact database. Content review date: 2026-08-23.
Save to your cloud drive
Save the complete collection first so files remain together and are easier to access across devices.
Quark Cloud Drive
RecommendedSave Natural Language Processing with Large Language Models Cheat Sheet to this cloud drive
Baidu Netdisk
Save Natural Language Processing with Large Language Models Cheat Sheet to this cloud drive
LLM NLP study guide
Define the task and error cost, prepare de-identified samples, choose prompting or retrieval based on evidence and preserve failed cases for evaluation.
Before you start
- Prepare a PDF reader and document text sources, language, length and sensitive fields.
- Prepare representative de-identified samples and a human review rubric.
- Decide the output schema, refusal conditions and citation expectations.
Quick start
- 01
Define task and output
Separate classification, extraction, summary, question answering and generation, and write input fields, output schema and acceptable errors.
- 02
Clean and segment text
Normalize encoding, remove unnecessary repetition, split long text by meaning and preserve source and paragraph identifiers.
- 03
Choose prompt or retrieval
Use structured prompting for simple tasks and add embeddings and retrieval for knowledge-heavy tasks, checking whether retrieved text supports the answer.
- 04
Evaluate failures
Measure correctness, completeness, citations, stability, cost and sensitive-data exposure, and preserve failed samples with configuration versions.
Usage tips
- Treat generated text as a hypothesis and return important claims to verifiable source material.
- Keep title, date and permission metadata with retrieved passages to avoid context-free answers.
- Set minimal retention and access rules separately for user input, logs, embeddings, indexes and outputs.
Troubleshooting and uninstall
What if an answer sounds reasonable but is factually wrong?
Add representative evaluation cases and refusal conditions, inspect retrieval and prompt constraints and route important claims through a human or rule check.
Why are long-document retrieval results irrelevant?
Check chunk size, overlap, metadata, query rewriting and retrieval count with labelled examples before adjusting generation instructions.
Frequently asked questions
What tasks does the LLM NLP sheet support?
It supports workflow lookup for classification, extraction, summarization, question answering, embeddings and retrieval-augmented generation.
When should retrieval augmentation be considered?
Consider it when answers rely on changing, large or permission-controlled knowledge, and evaluate relevance and completeness of retrieved material.
How should generated results be evaluated?
Combine task metrics, human review, factual consistency, refusal quality, stability, cost and privacy checks instead of language fluency alone.
What should be checked before uploading text?
Identify personal data, internal documents, credentials and copyrighted material, then de-identify, limit retention and confirm processing boundaries.