Data & Analytics Skill
Airflow DAG Patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and…
$15
RAG Implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases…
A production blueprint for Retrieval-Augmented Generation systems that ground LLM answers in your own documents instead of letting the model guess. It separates retrieval quality from generation quality so you can debug each layer independently, and ships with faithfulness-first prompting that forces the model to cite sources or say 'I don't have enough information' rather than hallucinate. The result is a knowledge assistant your users can actually trust.
$15 one-time
Add to a kit → Prices include 20% VAT. · Forged on real agency work · one-time, no lock-in
- Type Skill
- Category Data & Analytics
- Delivery Email · instant
- License One-time
Inside the run · no black box
See the actual work before you buy it.
When a RAG answer is wrong, the first question is which layer failed. Retrieval and generation are measured separately, with hybrid search, reranking, and a weekly benchmark that catches silent drift.
- Treats retrieval and generation as two separate problems with separate metrics: precision and recall for the retrieval side, faithfulness and relevance for the generation side. When quality drops, the logs isolate which layer failed instead of guessing at an end-to-end score.
- Chunks by use case, not by habit: 256 to 512 tokens for precise Q&A, 1000 to 2000 for analysis and summarization, and the parent-document pattern to get both, small chunks for matching, large parents for context. Overlap stays in the 10 to 20 percent band.
- Retrieves hybrid: BM25 keyword matching and dense embeddings fused with weighted rank fusion (typically 30 percent sparse, 70 percent semantic), pulling 20 to 50 candidates for high recall.
- Reranks before generating: a cross-encoder or rerank API reorders the candidates down to the final top-k, with MMR available when diversity matters. Skipping this stage typically costs 15 to 25 points of precision.
- Generates faithfulness-first: the prompt restricts answers to the provided context, citations are mandatory, and structured output carries a confidence score with an automatic 'not found in my sources' fallback below 0.5.
- Evaluates continuously: a fixed test set scores retrieval precision, recall and answer faithfulness every sprint, the embedding model version lives in metadata so an upgrade triggers a full re-embed (partial mixing of vector spaces is banned), and a weekly 50-query benchmark catches silent drift.
One power source. 6 lines out.
rag-implementation · core
core active · 6 lines
- ✓ document q&a over propri…
Document Q&A over proprietary knowledge bases
- ✓ chatbots that answer from
Chatbots that answer from current, factual sources
- ✓ natural-language semanti…
Natural-language semantic search
- ✓ documentation assistants…
Documentation assistants with source citations
- ✓ research tools that show
Research tools that show their references
- ✓ reducing hallucinations in
Reducing hallucinations in customer-facing AI
Yours to keep.
Drag time forward. Watch what stays.
Forever
That's what owning means.
The rented stack
ai writing tool: subscription
expired · access lostanalytics suite: subscription
expired · access lostdesign platform: subscription
expired · access lost(nothing left)
Your forge
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Answers grounded in your sources with inline citations, not invented facts
license: perpetual -
Independent debugging of retrieval vs generation, so you fix the real cause
license: perpetual -
Lower token spend through context-budget management and contextual compression
license: perpetual -
Measurable quality via precision, recall, and faithfulness metrics you can track in production
license: perpetual
subscriptions expire · deeds don't
Everything in the box.
Pick a piece up. Watch it work.
LangGraph retrieve-then-generate pipeline ready to run
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
Engineering teams building knowledge-grounded AI assistants, Q&A systems, or semantic search over their own documents.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
the air is clear. nothing between you and the forge.
catch a spark: the forge will answer
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Does this work with my existing vector database, or do I have to switch?
It ships vector store configs for Pinecone, Weaviate, Chroma, and pgvector, so on any of those you mostly wire in credentials. A different store means adapting the config yourself; the retrieval pipeline and chunking strategies stay the same.
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We already embed documents and stuff the top chunks into the prompt, where does that naive setup fall short of this blueprint?
It separates retrieval quality from generation quality so you can debug each layer on its own, and runs hybrid search that fuses BM25 with dense embeddings via Reciprocal Rank Fusion. The faithfulness-first prompting then forces the model to cite sources or say it lacks information instead of guessing.
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Will it fine-tune or train a model on my documents?
No. This is retrieval, not training: your documents sit in a vector store and get pulled into context at query time. The model's weights never change, which is also why your content stays portable across models.
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How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
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One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
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Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.