Build Rag Search Stacks

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.

---

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:

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](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?

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: hiteshbandhu
• Source: hiteshbandhu/skills-i-use
• License: MIT

Install and usage instructions live in the source repository linked above.