build-rag-search-stacks

Files (4)

• README.md
• SKILL.md
• source-index.md
• workflows.md

SKILL.md (first lines)

# 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.md) — workflows A–L (steps, deliverables, stop conditions)
- [source-index.md](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](https://github.com/hiteshbandhu/skills-i-use/blob/main/skills/OUTPUT.md).

---

## Step 0 — Pick workflow

Use 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.

---

## Install

Copy this folder into your agent’s skills directory, for example:

```bash
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](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 user

1. Name the workflow (A–L) and what you are producing
2. 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 corpus
help me evaluate web search APIs for agents
we need enterprise RAG — where do we start?
```

From hiteshbandhu/skills-i-use, MIT.

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