How do I install the Build RAG Search Stacks skill?

Run npx skillmds@latest add hiteshbandhu/build-rag-search-stacks in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Build RAG Search Stacks safe to use?

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Which AI agents work with Build RAG Search Stacks?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Build RAG Search Stacks free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Build RAG Search Stacks?

hiteshbandhu (@hiteshbandhu) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Build RAG Search Stacks by hiteshbandhu

Name: Build RAG Search Stacks
Author: hiteshbandhu

Build RAG Search Stacks

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".

hiteshbandhu Updated Sep 17, 2026

File contents

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?

---name: build-rag-search-stacksdescription: 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".---# Build RAG and AI search stacksAction playbook from thirteen AI Engineer talks. **Do not summarize talks** — pick a workflow and execute it.**Supporting files** (read when needed):- [workflows.md](workflows.md) — workflows A–L (steps, deliverables, stop conditions)- [source-index.md](source-index.md) — src-NNN → talk learnings in ingest-into-skillsOptional deliverables: `{SKILL_OUTPUT_DIR}/build-rag-search-stacks/` — see [skills-i-use OUTPUT.md](https://github.com/hiteshbandhu/skills-i-use/blob/main/skills/OUTPUT.md).---## Step 0 — Pick workflowUse the decision tree below. Open the matching section in [workflows.md](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.---## InstallCopy this folder into your agent’s skills directory, for example:```bashcp -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](https://github.com/hiteshbandhu/skills-i-use) or ingest-into-skills after sync.Source corpus: [ingest-into-skills](https://github.com/hiteshbandhu/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](source-index.md).---## Output to user1. Name the workflow (A–L) and what you are producing2. Save artifacts under `./skill-outputs/build-rag-search-stacks/` when the user wants files (diagrams, rubrics, checklists)3. Do not auto-commit---## Invocation examples```@build-rag-search-stacks design hybrid retrieval for our legal corpushelp me evaluate web search APIs for agentswe need enterprise RAG — where do we start?```

Frequently asked questions