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RAG Implementation · giuseppe-trisciuoglio | DecimalAI

ai mlby giuseppe-trisciuoglio

RAG Implementation

giuseppe-trisciuoglio/rag

Source giuseppe-trisciuoglio/developer-kit ↗License MIT

Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems.

SkillScore86Maintained

Automatic

AI Engineer pack

Last verified 8/21/2026

What it changes

Passed 19 of 22 test cases with the skill, and 16 of 22 without it.

Without skill73%

With skill86%

Checks it passed that the model alone missed

Configures minScore on the retriever builder
Recommends e5-large-v2 for multilingual retrieval
Recommends Chroma for lightweight local development

and 3 more in the full report

4 fixed15 passed either way2 still failing1 broke

Pass-rate lift: +14 pp
Tokens: +26%
Agent turns: 0%
Response time: -42%

Model gemini-3.6-flashMeasured 8/21/2026v119 of 22 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/giuseppe-trisciuoglio-rag/SKILL.md https://app.decimal.ai/s/giuseppe-trisciuoglio-rag/SKILL.md

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

decimalai skills add giuseppe-trisciuoglio-rag

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

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

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

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 4

Prompt

We need to set up a multi-source RAG retrieval system for customer support documentation. Could you design a Java blueprint that merges results across multiple embedding stores, applies metadata filtering, enforces a minimum similarity threshold, and re-orders the candidate contexts before feeding them to the chat model? Include the code configuration and retrieval workflow.

What was checked, without and with the skill
Checked	Without skill	With skill
Uses LangChain4j EmbeddingStoreContentRetriever for vector retrieval configuration	Missed	Passed
Configures minScore on the retriever builder	Missed	Passed
Re-orders retrieved contexts from multiple ContentRetriever sources using a reranker	Missed	Passed

case-01 · 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.md201 lines8 files

RAG Implementation

Build Retrieval-Augmented Generation systems that extend AI capabilities with external knowledge sources.

Overview

This skill covers: document processing, embedding generation, vector storage, retrieval configuration, and RAG pipeline implementation.

When to Use

Building Q&A systems over proprietary documents
Creating chatbots with factual information from knowledge bases
Implementing semantic search with natural language queries
Reducing hallucinations with grounded, sourced responses
Building documentation assistants and research tools
Enabling AI systems to access domain-specific knowledge

Instructions

Step 1: Choose Vector Database

Select based on your requirements:

Requirement	Recommended
Production scalability	Pinecone, Milvus
Open-source	Weaviate, Qdrant
Local development	Chroma, FAISS
Hybrid search	Weaviate with BM25

Step 2: Select Embedding Model

Use Case	Model
General purpose	text-embedding-ada-002
Fast and lightweight	all-MiniLM-L6-v2
Multilingual	e5-large-v2
Best performance	bge-large-en-v1.5

Step 3: Implement Document Processing Pipeline

Track record

Benchmark

19 / 22

test cases passed, vs 16 / 22 without

Adoption

Users: 0
Times used: 0

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

giuseppe-trisciuoglio / developer-kit

plugins/developer-kit-ai/skills/rag/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1Bundled 7 files

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See it in action

What's inside

RAG Implementation

Overview

When to Use

Instructions

Step 1: Choose Vector Database

Step 2: Select Embedding Model

Step 3: Implement Document Processing Pipeline

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

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