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Prompt Eng in eering Patterns That Actually Work in Production

Reusable prompt patterns to write for improved reliability, consistency and performance.

RK
Ritesh K.
AI Engineer, Botmartz · April 2, 2024 · 7 min read
◈ Open in Google Colab⌥ View Code on GitHub
Read Time
7 min
Failure Modes
5
Code Snippets
3
Runnable Notebook
1
Botmartz AI Insight
Evaluating Retrieval, Chunking, and Generation in Production
Retrieval QualityChunking StrategyHybrid SearchContinuous EvaluationGuardrailsTakeaways
# Prompt Engineering Patterns That Actually Work in Production Prompt engineering is more than just writing instructions. To achieve consistent, structured output from LLMs in enterprise systems, we rely on established prompt design patterns. ## Reusable Production Patterns - **Structured Output Constraints**: Enforcing JSON schemas directly in prompts. - **Few-Shot Delimiters**: Using specific tags to separate examples from inputs. - **Role-Based Framing**: Setting context parameters to limit response scope.

Closing Takeaways

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Measure retrieval precision and recall in isolation before touching the model.
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Chunk along document structure, not arbitrary character counts.
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Combine vector and keyword search — hybrid retrieval beats either alone.
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Treat evaluation as continuous infrastructure, not a launch-week report.
Try It Yourself
A runnable Google Colab notebook with the eval harness and hybrid search code from this post.
◈ Open in Colab⌥ GitHub Repo
#Enterprise RAG#Evaluation#Production AI#LangChain
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RK
Ritesh K.
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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