rag-implementation

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

2.12x

Quality

72%

Does it follow best practices?

Impact

70%

2.12x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./tests/ext_conformance/artifacts/agents-wshobson/llm-application-dev/skills/rag-implementation/SKILL.md

The canonical home for this skill is rag-implementation in wshobson/agents

Preview

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Run	Type	Date	Status
baseline vs usage-spec With / without context 7 months ago Completed	With / without context	7 months ago	Completed

Eval run

019cc843-e295-70d4-9f46-2e9232ef3417

Run 7 months ago

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Repository: Dicklesworthstone/pi_agent_rust
Path: tests/ext_conformance/artifacts/agents-wshobson/llm-application-dev/skills/rag-implementation/SKILL.md
Commit: a65ce00

Evaluated: 7 months ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Internal Documentation Q&A Assistant Technical Support Knowledge Base Search Scientific Literature Q&A with Quality Evaluation

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