ProductEngineer.pro
04.7 · Capability in Engineering

AI-native engineering

Building product features on top of language models and agents, where behavior is probabilistic and must be measured rather than assumed.

Map9 skills
LLM integrationStructuredoutputsRetrieval (RAG)AgentsTool callingModel ContextProtocolEvals anddatasetsAI observabilityGuardrailsData engineeringEngineering qualityPerformance engineering04.7 AI-native engineeringDeveloper experienceExplore ↗
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  • 04.7.1LLM integrationCalling language models from a product reliably.
  • 04.7.2Structured outputsGetting model responses in a reliable machine-readable format.
  • 04.7.3Retrieval (RAG)Giving a model the right context from your own data.
  • 04.7.4AgentsSystems where a model plans and takes multiple steps with tools.
  • 04.7.5Tool callingLetting a model call functions and APIs in your product.
  • 04.7.6Model Context ProtocolAn open standard for connecting AI applications to tools and data.
  • 04.7.7Evals and datasetsMeasuring AI behavior with test sets and repeatable evaluation runs.
  • 04.7.8AI observabilityTracing and inspecting what models and agents actually did in production.
  • 04.7.9GuardrailsChecks that keep AI output safe, on topic and within policy.
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Where to start

3 resources
  • articleStart here
    Building effective AI agents ↗
    Anthropic · 2024 · 12 min

    When a simple workflow beats an agent, and the handful of patterns that cover most real systems.

  • articleStart here
    Your AI product needs evals ↗
    Hamel Husain · 2024 · 18 min

    The practical case for evals, built from a real product, from simple assertions to human review of traces.

  • docsStart here
    Prompt engineering overview ↗
    Anthropic docs · 5 min

    Where to start when a model's output is not good enough, before changing models or adding complexity.

Tools

2 tools

Also in Engineering

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