Adding Context and Breaking Down Complex Prompts

Two of the most powerful prompting techniques are giving the AI extra context and breaking big tasks into smaller ones. Both approaches help the model give you much more accurate and useful answers.

Adding Context

AI models do not know everything about your situation. If you ask "How do I fix my disconnected WiFi?" you will get generic troubleshooting steps. But if you add context ("My Google Wifi router has a slowly blinking yellow light") and include the relevant troubleshooting guide, the model can give you the exact fix.

The rule: Never assume the model knows what you know. If there is specific information that matters, paste it into the prompt. Documents, manuals, code snippets, previous messages, anything relevant helps.

Breaking Down Complex Prompts

When your task has many steps or conditions, shoving everything into one prompt often leads to messy results. Instead, break it down using one of these three strategies:

1. Break Down Instructions

Instead of one prompt with ten instructions, create one prompt per instruction. Then choose which prompt to run based on the situation. Think of it as separate tools for separate jobs rather than a Swiss Army knife.

2. Chain Prompts Together

For tasks with sequential steps, make each step its own prompt. The output of step 1 becomes the input of step 2, and so on. This is like an assembly line, each station does one thing well and passes the result forward.

3. Aggregate Responses

When different parts of your data need different treatments, run separate prompts in parallel on each part, then combine the results. For example, process the first half of a document one way and the second half another way, then merge.

The pattern: Give context when the model lacks your specific knowledge. Break things down when the task is too complex for one prompt. Chain for sequential work, aggregate for parallel work.

Based on: Gemini API Prompting Strategies (ai.google.dev)