Prompt engineering — techniques, templates and reliability tactics
The Prompt Engineering section covers how to make LLM outputs reliable enough for production: prompt structures that hold up across edge cases, evaluation pipelines that catch regressions, retrieval patterns that ground answers in real data. Less about clever single prompts, more about systems that ship.
What's covered
Prompt patterns — system prompts with role + constraints + output format; few-shot vs. zero-shot trade-offs; chain-of-thought when it helps and when it just inflates token cost. Evaluation — automated eval suites, LLM-as-judge setups, regression tests that flag prompt drift. RAG — chunking strategies, embedding choice, re-ranking, hybrid search. Agents — tool calling, planning loops, fallback handling.
Examples use real-world tasks (extract structured data from invoices, route support tickets, summarise long meetings) so you can see the prompt-to-output relationship on familiar problems. Where relevant, posts compare outputs across ChatGPT, Claude and Gemini — same prompt, different models, different failure modes.
From prompt to production
A prompt that works once on a happy-path example is not a feature. The category emphasises reproducibility: prompts that hold up across thousands of calls, eval gates that catch the 1% of cases where the model goes off-script, monitoring that surfaces silent drift after a vendor model update.
For builders, not researchers
Posts target practitioners building production features, not academic prompt-research enthusiasts. The "is this prompt good?" question collapses into "does this prompt make my product work better?" with concrete metrics. If you're tuning prompts for a real product, this is the section to start with.