Prompt Techniques

Advanced techniques for getting better results from AI models. These methods go beyond basic prompting to help you leverage the full capabilities of modern AI systems.

When to Use Each Technique

Different problems call for different prompting approaches. Here is a framework for choosing the right technique:

Technique	Best For	Complexity
Agentic Prompting	Multi-step tasks requiring planning, tool use, and autonomous decision-making	High
Chain of Thought	Math, logic, reasoning problems that benefit from step-by-step thinking	Low
Prompt Chaining	Complex workflows that benefit from intermediate outputs and routing	Medium
Tool-Use Patterns	Tasks requiring external data, API calls, or structured actions	Medium
Agent Memory	Conversations or tasks requiring context across multiple interactions	Medium
RAG Patterns	Knowledge-intensive tasks that need external information retrieval	High

Techniques in This Section

Agentic Prompting - Multi-step AI workflows with planning and tool use
Chain-of-Thought - Step-by-step reasoning for complex problems
Prompt Chaining - Multi-step workflows with routing and parallelization
Tool-Use Patterns - Function-calling design patterns for reliable tool use
Agent Memory - Context management, RAG, episodic and persistent memory systems
RAG Patterns - Retrieval-Augmented Generation from naive to agentic

Quick Decision Guide

Ask yourself these questions to find the right technique:

Does the task require multiple steps? → Prompt Chaining or Agentic Prompting
Does it need reasoning or math? → Chain of Thought
Does it need external data? → RAG Patterns or Tool-Use Patterns
Does it need to remember past context? → Agent Memory
Does it need autonomous decision-making? → Agentic Prompting
Is it simple and well-defined? → Single direct prompt (no advanced technique needed)

How Techniques Build on Each Other

These techniques are not mutually exclusive. In practice, you will often combine them:

Chain of Thought + Tool Use — Reason step-by-step while fetching external data at each step
Agentic Prompting + Memory — Autonomous agents that remember past interactions
RAG + Prompt Chaining — Retrieve relevant documents, then pass them through a chain of processing steps
Agentic + RAG — Agents that autonomously decide when to retrieve information

Note:

Start simple. Use the simplest technique that solves your problem. Only add complexity when you hit clear limitations with the current approach.

Why Technique Matters

The way you prompt directly impacts output quality. Simple questions get simple answers. Structured techniques unlock deeper reasoning, better accuracy, and more useful responses.

A well-chosen technique can:

Reduce hallucinations by grounding the model in structured reasoning
Improve consistency across multiple generations
Enable complex multi-step tasks that single prompts cannot handle
Lower token usage by guiding the model toward concise, relevant outputs

Getting Started

If you are new to prompting techniques, start with Chain of Thought — it is the simplest to implement and works across almost all use cases. From there, explore Prompt Chaining if you need multi-step workflows, then graduate to Agentic Prompting when you need autonomous decision-making.

Note:

Pro Tip: Start with the simplest technique that works for your use case. Only escalate to more complex methods when basic prompting falls short.

When NOT to Use Each Technique

Technique	Avoid When	Better Alternative
Agentic Prompting	Task is simple and deterministic	Direct instruction prompting
Chain of Thought	Speed is critical (adds token overhead)	Direct answer with confidence scoring
Prompt Chaining	Single prompt can handle the task	Single well-structured prompt
Tool-Use Patterns	No external data or actions needed	Standard prompting with examples
Agent Memory	Each interaction is independent	Stateless prompting
RAG Patterns	Model already has sufficient knowledge	Direct prompting with relevant context

Prompt Techniques