Summary
This part explored practical techniques for working with large language models, from basic prompting principles to advanced strategies.
We first studied prompting basics and taxonomy. Prompts are instructions or queries to language models. Effective prompting follows key principles: clarity (specific, unambiguous instructions), context (relevant background information), examples (demonstrations of desired behavior), and structure (appropriate formatting).
We examined clear instructions and context. Specificity matters — vague prompts produce vague outputs. Providing relevant context helps models understand what you’re asking for and why. Role-playing (asking models to adopt specific perspectives), formatting instructions (specifying desired output structure), and step-by-step guidance improve results.
We briefly outlined in-context learning with zero-shot, one-shot, and few-shot prompting. Zero-shot relies on instructions alone, one-shot provides a single example to guide formatting or content, and few-shot offers multiple examples to demonstrate patterns.
We explored reference text and RAG (Retrieval-Augmented Generation). Providing reference documents grounds responses in specific information, reducing hallucination. RAG systems retrieve relevant information from knowledge bases before generating responses, enabling models to access current information beyond their training data and cite specific sources.
We studied reasoning through problems by guiding models to show step-by-step reasoning that improves performance on complex tasks requiring logic or mathematics, and briefly discussed reasoning models.
We examined prompt chaining — breaking complex tasks into sequences of simpler prompts where each step’s output becomes the next step’s input. This improves reliability for multi-stage workflows like analysis, decision-making, or content creation, though it increases latency and cost.
Finally, we examined fundamental limitations of prompting. No prompting technique can overcome models’ knowledge cutoffs, eliminate hallucination, fully address reasoning constraints, or eliminate training data biases. Understanding these limitations helps set appropriate expectations and choose suitable applications.
Key Takeaways
- Effective prompting takes effort.
- Clear instructions and context significantly improve output quality and relevance.
- Reference text and RAG ground responses in specific information, reducing hallucination and enabling source citation.
- Chain-of-thought prompting improves performance on complex reasoning tasks by showing step-by-step thinking.
- Prompt chaining breaks complex workflows into sequences of simpler prompts for improved reliability.
- Fundamental limitations include knowledge cutoffs, hallucination, reasoning constraints, and lack of true understanding — no prompting technique can fully overcome these.