"The 5 prompt patterns every AI developer should know"

After building dozens of AI features in production, I've found that 90% of prompt engineering comes down to 5 patterns:

1. Few-Shot with Edge Cases

Don't just show the model happy-path examples. Include the weird inputs, the empty strings, the malformed data. That's where most LLM pipelines break.

2. Role + Constraint Stacking

"You are X" isn't enough. Stack constraints: "You are X. You NEVER do Y. If Z happens, you always respond with W." The more specific your guardrails, the more reliable the output.

3. Output Schema Enforcement

Always define the exact JSON shape you expect. Better yet, use structured output mode when available. Parsing free-text LLM responses in production is a nightmare.

4. Chain of Verification

Have the model generate an answer, then ask it to verify its own answer against specific criteria. Catches hallucinations before they reach your users.

5. Dynamic Context Injection

Stop putting everything in the system prompt. Inject only the relevant context per request using RAG or dynamic template filling. Smaller, focused context = better outputs.

I break these down with real production examples inside Prompt Engineers. If you're shipping AI products, this is the community for you.

I've spent the last year building AI-powered apps and I've noticed something: most developers treat LLMs like magic text boxes.

They paste in vague prompts, get mediocre outputs, and blame the model. But the problem isn't the model — it's the engineering.

Here's what actually moves the needle:

1. Structured outputs beat freeform every time. Instead of "write me a function that does X," give the model a schema, examples, and constraints. Your output quality jumps 3-5x.

2. Chain-of-thought isn't just a technique — it's a debugging tool. When your agent fails, making it reason step-by-step reveals exactly where the logic breaks.

3. The best AI apps are 80% engineering, 20% prompting. Retrieval pipelines, error handling, fallback logic, caching — that's where the real value is. The prompt is the tip of the iceberg.

4. Evaluation is everything. If you can't measure whether your prompts are getting better, you're guessing. Build evals before you build features.

I started Prompt Engineers to create a space where devs share what's actually working — real prompt patterns, production architectures, and honest breakdowns of what fails.

If you're building with AI and want to ship faster, this is the spot.