Enterprise Search

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.

Search Architecture Patterns

Architecture Comparison

Connector Architecture

Access Control

Critical requirement: Search results must respect source-level permissions.

Knowledge Taxonomy Design

Metadata Schema

Every indexed document should carry these metadata fields:

Tagging Standards

Content Freshness Policies

Relevance Tuning Framework

Scoring Factors

Field Boosting

Query Understanding

Search Quality Metrics

Core Metrics

Quality Improvement Loop

1. Sample 100 queries weekly from search logs
2. Have 2+ raters judge relevance of top 10 results (0-3 scale)
3. Calculate MRR, NDCG@10, Precision@5
4. Identify failure patterns (categories of bad results)
5. Adjust relevance model (boosting, synonyms, freshness weights)
6. A/B test changes against baseline
7. Repeat monthly

Search UX Patterns

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]

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

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