Build rag search stacks
Skill hiteshbandhu/skills-i-use/skills/ai-engineer-talks/build-rag-search-stacks
Drop-in skills and plugins for your AI development workflows
npx -y skills add hiteshbandhu/skills-i-use --skill build-rag-search-stacksAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Runs checklists and workflows for designing, layering, evaluating, and operating production RAG and AI search — hybrid retrieval, agentic context tools, enterprise eval, web neural search, managed platforms, document agents. Use when the user builds retrieval for agents, picks vector vs lexical vs hybrid, designs eval for augmented AI, scales regulated RAG, compares search APIs, or says "RAG stack", "hybrid search", "agentic RAG", "vector benchmark".
SKILL.md
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Build RAG and AI search stacks
Action playbook from thirteen AI Engineer talks. Do not summarize talks — pick a workflow and execute it.
Supporting files (read when needed):
- workflows.md — workflows A–L (steps, deliverables, stop conditions)
- source-index.md — src-NNN → talk learnings in ingest-into-skills
Optional deliverables: {SKILL_OUTPUT_DIR}/build-rag-search-stacks/ — see skills-i-use OUTPUT.md.
Step 0 — Pick workflow
Use the decision tree below. Open the matching section in workflows.md.
What is the user trying to do?
├─ Choose RAG vs long-context vs fine-tune → A
├─ Design hybrid retrieval (lexical + vector + rerank) → B
├─ Vet vector DB / ANN vendor claims → C
├─ Ship regulated / legal / enterprise corpus RAG → D
├─ Layer techniques without over-building → E
├─ Build agent tools for multi-source context → F
├─ Persist memory + tool scale for agents → G
├─ Evaluate web / augmented AI search in production → H
├─ Open-web research agent (long semantic queries) → I
├─ Fast enterprise doc Q&A (tables, grounding) → J
├─ Document-heavy knowledge work (Excel, SharePoint) → K
└─ Vertical product KB (sales, onboarding assets) → L
Stop summarizing once a workflow is identified — run its checklist.
Install
Copy this folder into your agent’s skills directory, for example:
cp -r skills/ai-engineer-talks/build-rag-search-stacks ~/.claude/skills/
cp -r skills/ai-engineer-talks/build-rag-search-stacks ~/.cursor/skills/
cp -r skills/ai-engineer-talks/build-rag-search-stacks ~/.codex/skills/
From skills-i-use or ingest-into-skills after sync.
Source corpus: ingest-into-skills playlists/rag-search-2025/.
Cross-cutting rules
| Rule | Source |
|---|---|
| Embeddings are the dominant lever in most stacks | [src-009 @ 4:11] |
| AI search = concept retrieval + intent + filters + agent loops | [src-009 @ 2:27] |
| Instruction-tuned / steerable embeddings for query shape | [src-009 @ 10:37] |
| Agentic loop: decompose → search → LLM expand → search again | [src-009 @ 7:25] |
| Contextual retrieval: enrich chunks with titles/global metadata | [src-010 @ 8:29] |
| Multimodal: screenshot → multimodal embedding for slides/tables | [src-010 @ 15:07] |
Disputed steps: read talk in source-index.md.
Output to user
- Name the workflow (A–L) and what you are producing
- Save artifacts under
./skill-outputs/build-rag-search-stacks/when the user wants files (diagrams, rubrics, checklists) - Do not auto-commit
Invocation examples
@build-rag-search-stacks design hybrid retrieval for our legal corpus
help me evaluate web search APIs for agents
we need enterprise RAG — where do we start?