Enterprise Search

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name: enterprise-search description: Design cross-tool knowledge retrieval strategies, architect enterprise search systems, and tune relevance models. Use when building internal search experiences, consolidating knowledge across tools, or improving search result quality. tags: [search, knowledge-management, information-retrieval] ---

Enterprise Search

Business-oriented framework for designing cross-tool knowledge retrieval, architecting enterprise search systems, and tuning relevance models. Focused on strategy and requirements — for technical implementation, see hybrid-search-implementation and similarity-search-patterns in the ai-ml domain.

Use this skill when

• Designing an enterprise search strategy across multiple internal tools (Confluence, Slack, Drive, SharePoint, GitHub)
• Choosing between federated, centralized, or hybrid search architectures
• Defining relevance tuning requirements and quality metrics
• Building a knowledge taxonomy or metadata schema for searchable content
• Creating search UX requirements for internal portals
• Evaluating search quality and measuring improvement

Do not use this skill when

• Implementing vector search or embeddings at code level (use hybrid-search-implementation)
• Building similarity search with specific vector databases (use similarity-search-patterns)
• Optimizing web SEO for external search engines (use seo-audit)
• Building RAG pipelines for LLM applications (use RAG skills in ai-ml domain)

Instructions

1. Audit current state — inventory all content sources, volumes, and access patterns.
2. Choose architecture — federated, centralized, or hybrid based on your constraints.
3. Design taxonomy — define metadata schema, facets, and tagging standards.
4. Define relevance model — scoring factors, boosting rules, and personalization signals.
5. Set quality metrics — establish baselines and targets for search quality.
6. Design search UX — autocomplete, facets, snippets, and result presentation.

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Search Architecture Patterns

Architecture Comparison

| Pattern | How It Works | Pros | Cons | Best For | |---------|-------------|------|------|----------| | Federated | Query multiple sources in real-time, merge results | No data duplication, real-time freshness | Slower, limited cross-source ranking | Small orgs (6 months | Flag for review | | Meeting notes | Permanent | N/A | Reduce ranking weight over time | | Code / PRs | Always current (live sync) | N/A | N/A | | Tickets / Issues | Live sync | N/A | Archive closed items after 12 months | | Policies / Runbooks | Updated semi-annually | >12 months | Alert content owner |

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Relevance Tuning Framework

Scoring Factors

| Factor | Weight | Description | |--------|--------|-------------| | Text relevance (BM25) | 40% | Keyword match quality — title, body, tags | | Freshness | 20% | More recent content ranked higher (decay function) | | Popularity | 15% | View count, link count, citation count | | Personalization | 15% | User's team, recent searches, frequently accessed sources | | Source authority | 10% | Official docs > Slack messages > personal notes |

Field Boosting

| Field | Boost Factor | Rationale | |-------|-------------|-----------| | Title | 3.0x | Titles are the strongest relevance signal | | Tags | 2.0x | Curated metadata is high-signal | | Headings (H1-H3) | 1.5x | Section headers indicate topic boundaries | | Body text | 1.0x | Baseline — full content match | | Comments | 0.5x | Noisy, often tangential |

Query Understanding

| Technique | Purpose | Example | |-----------|---------|---------| | Synonym expansion | Match equivalent terms | "deploy" → "deploy, release, ship" | | Spell correction | Handle typos | "kuberntes" → "kubernetes" | | Intent classification | Route to specialized search | "how do I deploy" → tutorial filter | | Entity recognition | Boost specific entities | "John's PR for auth" → person + code filter |

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Search Quality Metrics

Core Metrics

| Metric | Formula | Target | How to Measure | |--------|---------|--------|---------------| | MRR (Mean Reciprocal Rank) | Average of 1/rank of first relevant result | >0.6 | Relevance judgments on sample queries | | NDCG@10 | Normalized discounted cumulative gain at position 10 | >0.7 | Graded relevance judgments | | Precision@5 | % of top 5 results that are relevant | >60% | Binary relevance judgments | | Zero-Result Rate | % of queries returning no results | 40% | Click tracking | | Query Reformulation Rate | % of searches followed by a refined query | 80% |

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Output Template: Enterprise Search Requirements Document

# Enterprise Search Requirements — [Project Name]

## Current State
- **Content sources:** [list with estimated volumes]
- **Current search tools:** [what people use today]
- **Top pain points:** [from user interviews]

## Architecture Decision
- **Pattern:** [Federated / Centralized / Hybrid]
- **Rationale:** [why this pattern]
- **Search platform:** [Elasticsearch, Typesense, Algolia, Vespa, etc.]

## Scope (Phase 1)
- **Sources to index:** [list with priority]
- **Content types:** [documents, conversations, code, tickets]
- **Users:** [target audience and access model]

## Relevance Model
- **Scoring factors:** [weights per factor]
- **Field boosting:** [title, tags, headings, body]
- **Freshness decay:** [function and parameters]

## Quality Targets
| Metric | Baseline | Target |
|--------|----------|--------|
| MRR | [current] | [goal] |
| Zero-result rate | [current] | <5% |
| p95 latency | [current] | <1s |

## Roadmap
- Phase 1: [Core sources, basic search] — [timeline]
- Phase 2: [Additional sources, relevance tuning] — [timeline]
- Phase 3: [Personalization, AI-powered features] — [timeline]

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Common Mistakes

• Indexing everything without curation — more content does not mean better search; noisy sources dilute quality
• Ignoring access control — leaking confidential documents through search is a security incident
• No freshness weighting — returning 3-year-old docs before this week's update frustrates users
• Not measuring search quality — if you don't measure MRR/NDCG, you can't improve
• Building search without user research — understand what people actually search for before designing the system
• Treating search as a one-time project — relevance tuning is ongoing; plan for continuous improvement

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Additional Resources

• Related skills: hybrid-search-implementation (ai-ml — technical implementation), similarity-search-patterns (ai-ml — vector search)
• Elasticsearch / OpenSearch — open-source search engines
• Algolia — managed search platform
• Vespa — open-source search and recommendation engine

Source & license

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

• Author: frank-luongt
• Source: frank-luongt/faos-skills-marketplace
• License: Apache-2.0
• Homepage: https://faosx.ai/skills

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