Getting Started
Prompt engineering is the skill that separates agents that work from agents that stumble. Every interaction your agent has with an LLM is ultimately a prompt, so learning to write effective prompts is foundational to everything else on this roadmap.
The core insight is that LLMs are pattern-completion engines. Your job is to set up a pattern that leads to the output you want. Start by experimenting directly in a chat interface to build intuition, then move to structured prompts you can use programmatically.
There are four major techniques you need to master: zero-shot prompting, few-shot prompting, chain-of-thought reasoning, and role prompting. Each has strengths for different situations.
Key Concepts
Chain-of-thought prompting is the most important technique for agents. By asking the model to reason step-by-step, you dramatically improve accuracy on complex tasks:
Analyze this security log entry and determine if it represents a threat.
Think through this step by step:
1. Identify the source IP and action taken
2. Check if the pattern matches known attack signatures
3. Assess the severity level
4. Provide your verdict with confidence level
Log entry: {log_entry}
Few-shot prompting gives the model concrete examples to follow. This is essential when you need consistent output formatting:
Extract structured data from the product description.
Example input: "The Nike Air Max 90 in white, size 10, retails for $130"
Example output: {"brand": "Nike", "model": "Air Max 90", "color": "white", "size": "10", "price": 130}
Example input: "Adidas Ultraboost 22, black colorway, $190, size 9.5"
Example output: {"brand": "Adidas", "model": "Ultraboost 22", "color": "black", "size": "9.5", "price": 190}
Now extract from: {input_text}
Role prompting shapes the model's behavior by establishing expertise and constraints. This is how you control the personality and boundaries of your agents:
You are a senior database administrator with 15 years of experience.
You only provide SQL that is compatible with PostgreSQL 15+.
When asked about destructive operations, always include a warning and suggest a backup first.
Hands-On Practice
The best way to improve at prompting is systematic experimentation. Pick a task, write a prompt, test it with multiple inputs, and iterate. Keep track of what works and what does not. Common failure modes to watch for:
- Ambiguous instructions lead to inconsistent outputs. Be specific about format, length, and structure.
- Missing constraints let the model hallucinate or go off-topic. Always specify what the model should not do as well as what it should.
- Over-complicated prompts can confuse the model. If your prompt is longer than a page, consider breaking the task into smaller steps.
Build your prompt library as a living document that grows with your experience. You will reuse these patterns constantly when building agents.