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Most Agent failures aren’t model problems. They’re context problems. If you give vague or incomplete info, the Agent will fill in the blanks—and usually get them wrong. Here’s how to write high-context prompts that actually work 👇

Prompting isn’t about being clever. It’s about giving the Agent everything it needs to not guess. That means: - Clear goals - Relevant files - Helpful examples - Precise constraints Agents don’t hallucinate randomly—they hallucinate from missing context.

Here’s what a low-context prompt looks like: “Enable JSON parser for chat backend.” Sounds fine, right? Until the Agent: - Picks the wrong file - Misses the LLMOutputParsing class - Uses a config that doesn’t exist Because you didn’t ground it.

A high-context prompt answers the silent questions: - Where should I look? - What else is relevant? - Are there examples I can follow? - What’s the user really trying to do? Agents don’t ask. So you have to pre-answer.

Examples work too—but only if they’re scoped: ❌ “Implement tests for ImageProcessor.” ✅ “Implement tests for ImageProcessor. Follow the pattern in text_processor.py.” Now it knows what “good” looks like.

TL;DR: 🛠️ Tools help 📁 File paths help 🧠 Rationale helps 📌 Examples help Agents don’t need perfect prompts. They need complete ones.