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RAG Implementation · microck | DecimalAI

ai mlby microck

RAG Implementation

microck/rag-implementation

Source microck/ordinary-claude-skills ↗License NOASSERTION

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

SkillScore91Maintained

Automatic

AI Engineer pack

Last verified 8/21/2026

What it changes

Passed 21 of 23 test cases with the skill, and 19 of 23 without it.

Without skill83%

With skill91%

Checks it passed that the model alone missed

Configures chunk_size around 512 and chunk_overlap around 50
Instructs the LLM to output a percentage confidence score for its answer

2 fixed19 passed either way2 still failing0 broke

Pass-rate lift: +9 pp
Tokens: +115%
Agent turns: 0%
Response time: -15%

Model gemini-3.6-flashMeasured 8/21/2026v121 of 23 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/microck-rag-implementation/SKILL.md https://app.decimal.ai/s/microck-rag-implementation/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add microck-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/microck-rag-implementation/ and pins it in .decimal/skills.lock

decimalai skills add microck-rag-implementation

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

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

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

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 2

Prompt

When splitting source text strictly by token count using LangChain's TokenTextSplitter, what are typical default chunk size and overlap parameters?

What was checked, without and with the skill
Checked	Without skill	With skill
Uses TokenTextSplitter for token-based chunking	Passed	Passed
Configures chunk_size around 512 and chunk_overlap around 50	Missed	Passed

case-17 · gemini-3.6-flash · 8/21/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md398 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, fast queries
Weaviate: Open-source, hybrid search
Milvus: High performance, on-premise
Chroma: Lightweight, easy to use
Qdrant: Fast, filtered search
FAISS: Meta's library, local deployment

2. Embeddings

Purpose: Convert text to numerical vectors for similarity search

Models:

text-embedding-ada-002 (OpenAI): General purpose, 1536 dims
all-MiniLM-L6-v2 (Sentence Transformers): Fast, lightweight
e5-large-v2: High quality, multilingual
Instructor: Task-specific instructions
bge-large-en-v1.5: SOTA performance

3. Retrieval Strategies

Approaches:

Dense Retrieval: Semantic similarity via embeddings

Track record

Benchmark

21 / 23

test cases passed, vs 19 / 23 without

Adoption

Users: 2
Times used: 33

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

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

microck / ordinary-claude-skills

skills_all/rag-implementation/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/3/2026Version 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

3. Retrieval Strategies

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

Related skills