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RAG Implementation · dicklesworthstone | DecimalAI

ai mlby dicklesworthstone

RAG Implementation

dicklesworthstone/rag-implementation

Source dicklesworthstone/pi_agent_rust ↗License NOASSERTION

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.

SkillScore92Maintained

Automatic

AI Engineer pack

Last verified 8/29/2026

What it changes

Passed 23 of 25 test cases with the skill, and 16 of 25 without it.

Without skill64%

With skill92%

Checks it passed that the model alone missed

Include answer attribute typed as str.
Pass text_key="content" to WeaviateVectorStore.
Select voyage-code-3 for code repository search.

and 8 more in the full report

8 fixed15 passed either way1 still failing1 broke

Pass-rate lift: +28 pp
Tokens: +215%
Agent turns: 0%
Response time: -10%

Model gemini-3.6-flashMeasured 8/29/2026v123 of 25 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/dicklesworthstone-rag-implementation/SKILL.md https://app.decimal.ai/s/dicklesworthstone-rag-implementation/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add dicklesworthstone-rag-implementation, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/dicklesworthstone-rag-implementation/ and pins it in .decimal/skills.lock

decimalai skills add dicklesworthstone-rag-implementation

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/dicklesworthstone-rag-implementation/SKILL.md https://app.decimal.ai/s/dicklesworthstone-rag-implementation/SKILL.md

Raw file: /s/dicklesworthstone-rag-implementation/SKILL.md ↗ · machine-readable summary at /s/dicklesworthstone-rag-implementation.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 8

Prompt

When initializing WeaviateVectorStore in LangChain to connect to a local or cloud Weaviate database, which keyword argument specifies the document content property name?

What was checked, without and with the skill
Checked	Without skill	With skill
Pass text_key="content" to WeaviateVectorStore.	Missed	Passed

case-20 · gemini-3.6-flash · 8/29/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md565 linessingle file

RAG Implementation

Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.

When to Use This Skill

Building Q&A systems over proprietary documents
Creating chatbots with current, factual information
Implementing semantic search with natural language queries
Reducing hallucinations with grounded responses
Enabling LLMs to access domain-specific knowledge
Building documentation assistants
Creating research tools with source citation

Core Components

1. Vector Databases

Purpose: Store and retrieve document embeddings efficiently

Options:

Pinecone: Managed, scalable, serverless
Weaviate: Open-source, hybrid search, GraphQL
Milvus: High performance, on-premise
Chroma: Lightweight, easy to use, local development
Qdrant: Fast, filtered search, Rust-based
pgvector: PostgreSQL extension, SQL integration

2. Embeddings

Purpose: Convert text to numerical vectors for similarity search

Models (2026):

Model	Dimensions	Best For
voyage-3-large	1024	Claude apps (Anthropic recommended)
voyage-code-3	1024	Code search
text-embedding-3-large	3072	OpenAI apps, high accuracy
text-embedding-3-small	1536	OpenAI apps, cost-effective

Track record

Benchmark

23 / 25

test cases passed, vs 16 / 25 without

Adoption

Users: 2
Times used: 40

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

dicklesworthstone / pi_agent_rust

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

View on GitHub ↗

License NOASSERTIONImported 8/10/2026Version v1

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

RAG Implementation

When to Use This Skill

Core Components

1. Vector Databases

2. Embeddings

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

Related skills