Prompt Engineering
The practice of designing and iterating on LLM input instructions to reliably produce desired outputs for a specific task.
Prompt engineering is the highest-leverage skill for teams building with LLMs. A well-crafted prompt can eliminate 80% of edge cases, enforce consistent output formats, and dramatically improve response quality — all without any model training.
The core techniques include system prompts (setting overall behavior), few-shot examples (showing the model what you want), chain-of-thought reasoning (asking the model to think step by step), and structured output instructions (specifying JSON schemas or XML tags for parseable responses). Advanced techniques add guardrails, self-consistency checks, and multi-step reasoning chains.
Effective prompt engineering is iterative. Start with a basic prompt, test against 50+ real-world examples, identify failure modes, add specific instructions to handle them, and repeat. Most production prompts go through 10-20 iterations. The key insight: prompts are code. They should be version-controlled, tested against regression suites, and A/B tested just like any other code change that affects user experience.
Related Terms
LLM (Large Language Model)
A neural network trained on massive text corpora that can generate, understand, and transform natural language for tasks like summarization, classification, and conversation.
Fine-Tuning
The process of further training a pre-trained LLM on a domain-specific dataset to specialize its behavior, style, or knowledge for a particular task.
Tokenization
The process of splitting text into smaller units (tokens) that an LLM can process, typically subword pieces averaging about 4 characters per token.
RAG (Retrieval-Augmented Generation)
A technique that grounds LLM responses in external data by retrieving relevant documents at query time and injecting them into the prompt context.
Embeddings
Dense vector representations of text, images, or other data that capture semantic meaning in a high-dimensional space, enabling similarity search and clustering.
Vector Database
A specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings with sub-millisecond similarity search.
Further Reading
Prompt Engineering in 2026: What Actually Works
Forget the 'act as an expert' templates. After shipping dozens of LLM features in production, here are the prompt engineering techniques that actually improve outputs, reduce costs, and scale reliably.
Fine-tuning vs Prompting: The Real Trade-offs
An honest look at when each approach makes sense, with real cost comparisons and performance data.