Enterprise Search

Enterprise Search Engineering

Search infrastructure design, relevance tuning, query understanding, index management, quality measurement, and performance optimization. Each mode loads its own reference files on demand.

Mode Detection

Classify into one mode before proceeding.

| Mode | Signal Phrases | Reference |

|------|---------------|-----------|

| RELEVANCE | tune relevance, BM25, boost, function score, field weight, LTR, learned ranking, ranking model | references/relevance-tuning.md |

| QUERY | query understanding, intent classification, entity extraction, query expansion, synonyms, spell correction, query rewriting | references/query-understanding.md |

| INDEX | schema design, mapping, analyzer chain, reindex, alias, ILM, index template, field type | references/index-management.md |

| QUALITY | nDCG, MRR, precision, recall, search quality, judgment, A/B test, evaluation, search funnel | references/search-quality.md |

| PERFORMANCE | slow query, shard, cache, circuit breaker, scroll, search_after, query optimization, latency | references/performance-optimization.md |

| ARCHITECTURE | search architecture, hybrid search, vector search, pipeline design, platform selection, migration | (cross-reference: load relevant references based on sub-topic) |

If the request spans modes, pick the primary and note the secondary. ARCHITECTURE mode loads references from whichever sub-topics apply.

Workflow by Mode

RELEVANCE Mode

Load: references/relevance-tuning.md, references/llm-search-failure-modes.md

• Diagnose — Identify the relevance problem before tuning.

| Problem Class | Symptoms | Starting Point |

|--------------|----------|----------------|

| Poor precision | Good results buried under noise | Field boosting, minimum_should_match |

| Poor recall | Known-good results missing | Analyzer tuning, query expansion, synonym filters |

| Wrong ordering | Right results, wrong rank | BM25 parameter tuning, function scoring |

| Domain mismatch | Generic scoring fails domain | Learned ranking (LTR), custom similarity |

| Freshness | Stale results ranked too high | Decay functions, recency boosts |

• Baseline — Capture current relevance metrics before making changes. Minimum: nDCG@10, P@5, MRR on a representative judgment set. No tuning without a baseline.

• Tune — Apply changes from the reference. One variable at a time. Measure after each change.

| Tuning Layer | Tools | When to Use |

|-------------|-------|-------------|

| Analyzer chain | Tokenizers, filters, char_filters | Recall problems, morphological mismatch |

| Field boosting | Multi-match boosts, cross_fields | Some fields matter more than others |

| BM25 parameters | k1, b per field | Content-type-specific term saturation |

| Function scoring | Decay, field_value_factor, script_score | Non-textual relevance signals (popularity, freshness, authority) |

| Rescoring | rescore query with window_size | Expensive scoring on top-N candidates |

| Learned ranking (LTR) | Feature engineering, model training, SLTR plugin | BM25 + hand-tuned boosts plateau |

• Validate — Compare against baseline. Accept only statistically significant improvements. Check for regression on other query classes.

Gate: Baseline metrics captured. Each tuning change measured independently. No "tuned several things and it got better" — isolate the effect.

QUERY Mode

Load: references/query-understanding.md, references/llm-search-failure-modes.md

• Classify query intent — Determine what the user wants before constructing the query.

| Intent | Example | Query Strategy |

|--------|---------|---------------|

| Navigational | "OpenSearch documentation" | Exact match, title boost, URL matching |

| Informational | "how to configure sharding" | Full-text across body fields, snippet extraction |

| Transactional | "buy enterprise license" | Product/SKU fields, availability filters |

| Faceted | "red shoes size 10" | Structured filters + text scoring |

| Exploratory | "machine learning applications" | Broad match, diversified results, related terms |

• Extract entities — People, products, dates, categories, attributes from the query string.

• Transform — Apply query expansion, spelling correction, synonym injection, and relaxation strategies from the reference.

• Construct — Build the platform-specific query DSL. Include:
• bool query structure (must/should/filter/must_not)
• Field selection and boosting
• Filters vs scoring clauses (filters for hard constraints, scoring for ranking signals)
• Aggregations for facets

• Test — Validate against known queries. Check that transformations improve recall without destroying precision.

Gate: Query pipeline handles the 5 intent types. Entity extraction covers the domain vocabulary. Expansion and relaxation strategies are measurable.

INDEX Mode

Load: references/index-management.md, references/llm-search-failure-modes.md

• Requirements — Gather before designing.

| Question | Why It Matters |

|----------|---------------|

| Document count and growth rate | Shard count, ILM policy |

| Average document size | Shard sizing, bulk indexing batch size |

| Query patterns | Which fields need text analysis vs keyword vs numeric |

| Update frequency | Near-real-time vs batch, refresh interval |

| Retention policy | ILM phases, rollover triggers |

| Access patterns | Hot/warm/cold architecture, read vs write ratio |

• Design schema — Map fields with appropriate types. Use the reference for type selection guidance.

• Configure analyzers — Build analyzer chains for each text field. Standard analyzer is a starting point, not a solution.

• Template and alias — Set up index templates for consistent creation. Use aliases for zero-downtime operations.

• Reindex strategy — Plan for schema evolution. Reindexing is inevitable; design for it.

Gate: Schema covers all query-time field requirements. Analyzer chains tested against representative content. Alias strategy supports zero-downtime reindexing.

QUALITY Mode

Load: references/search-quality.md, references/llm-search-failure-modes.md

• Define metrics — Select metrics appropriate to the use case.

| Metric | Measures | Best For |

|--------|----------|----------|

| nDCG@k | Graded relevance at rank k | Rankings with multiple relevance levels |

| MRR | Position of first relevant result | Navigational queries, single-answer |

| P@k | Fraction relevant in top k | Precision-critical applications |

| Recall@k | Fraction of relevant docs found in top k | Recall-critical applications (legal, compliance) |

| MAP | Average precision across recall levels | Balanced precision/recall |

• Collect judgments — Build the ground truth dataset.

| Method | Scale | Quality | Cost |

|--------|-------|---------|------|

| Expert annotation | Small (100s) | Highest | High |

| Click logs | Large (10K+) | Moderate (position bias) | Low |

| Crowdsourcing | Medium (1K+) | Variable | Medium |

| LLM-assisted | Medium-Large | Good for initial pass, needs validation | Low |

• Evaluate — Run offline evaluation. Compare configurations. Report metrics with confidence intervals.

• Online testing — A/B test or interleave changes against production. Measure engagement metrics alongside relevance metrics.

• Monitor — Continuous quality dashboards. Alerting on metric degradation. Search funnel analysis (query -> click -> conversion).

Gate: Judgment set exists with documented guidelines. Offline metrics computed with confidence intervals. Online test plan specifies primary metric, minimum detectable effect, and sample size.

PERFORMANCE Mode

Load: references/performance-optimization.md, references/llm-search-failure-modes.md

• Profile — Identify the bottleneck before optimizing.

| Symptom | Likely Cause | Diagnostic |

|---------|-------------|------------|

| High p99 latency | Slow queries, GC pauses, shard imbalance | Slow query log, node stats, hot threads |

| Throughput ceiling | Undersized thread pools, too many shards | Thread pool stats, shard count per node |

| Memory pressure | Field data, too many aggregations, deep pagination | Node stats, circuit breaker trips |

| Indexing lag | Merge throttling, refresh overhead, slow pipelines | Index stats, merge stats |

| Cluster instability | Split brain, disk watermarks, master storms | Cluster health, allocation explain |

• Diagnose — Use platform diagnostics to confirm the cause. Measure before changing.

• Optimize — Apply targeted fixes from the reference. One change at a time, measure the effect.

• Validate — Load test the change. Check that optimization does not degrade other metrics (latency vs throughput tradeoff, cache hit rate vs memory).

Gate: Bottleneck identified with evidence. Fix targeted at root cause. Load test confirms improvement without regression.

ARCHITECTURE Mode

Cross-cutting mode. Load references based on the specific question.

• Platform evaluation — When selecting or migrating between platforms.

| Platform | Strengths | Consider When |

|----------|-----------|--------------|

| Elasticsearch/OpenSearch | Mature ecosystem, Lucene-based, strong text search | General-purpose search, log analytics |

| Vespa | Built-in ML serving, tensor computation, real-time updates | ML-heavy ranking, large-scale recommendations |

| Typesense | Simple API, typo tolerance, easy setup | Developer-facing search, smaller datasets |

| Solr | Configurable, NRT, strong faceting | Legacy integration, specific Solr features |

| Meilisearch | Instant search, typo-tolerant, developer-friendly | Frontend search, prototyping |

| Custom (Lucene/Tantivy) | Full control, embedded | Specialized needs, tight integration |

• Hybrid search design — When combining keyword and vector retrieval.

| Strategy | How | Tradeoff |

|----------|-----|----------|

| Score fusion (RRF) | Reciprocal rank fusion of BM25 + vector results | Simple, no training needed. Weights are heuristic. |

| Linear combination | Weighted sum of normalized BM25 + vector scores | Tunable. Requires score normalization. |

| Re-ranking | BM25 retrieval -> vector re-rank top N | Efficient. Vector search only on candidates. |

| Two-stage | Coarse retrieval (either) -> fine-grained re-rank (LTR) | Best quality. Most complex. |

• Pipeline design — Ingestion, enrichment, indexing, query, ranking pipelines.

• Migration planning — Version upgrades, platform changes, zero-downtime strategies.

LLM Failure Modes in Search Engineering

Load references/llm-search-failure-modes.md for all modes. These are the specific ways LLMs fail at search tasks:

| Failure Mode | What Happens | Defense |

|-------------|-------------|---------|

| Hallucinated query DSL | LLM invents plausible-looking query syntax that does not exist | Validate every query against the specific platform version's API docs |

| Version confusion | Mixing Elasticsearch 7.x and 8.x APIs, or ES and OpenSearch syntax | State the exact platform and version upfront. Reference version-specific docs. |

| Generic relevance advice | "Improve your relevance by boosting important fields" without specifics | Require concrete field names, boost values, and expected metric impact |

| Vector search as default | Recommending embeddings when BM25 with good analyzers solves the problem | Start with BM25 tuning. Vector search adds complexity; justify the added value. |

| Ignoring measurement | Suggesting changes without a quality measurement framework | Require baseline metrics before any tuning recommendation |

| Deprecated feature suggestions | Recommending removed or deprecated APIs (type mappings, indices.optimize) | Check the deprecation/migration guide for the target version |

| Over-engineered schemas | Adding 50 fields with sub-fields when 10 fields cover the queries | Schema complexity should match query requirements, not data model completeness |

| Cargo-cult configuration | Copying cluster settings from blog posts without understanding the workload | Every configuration value should have a justification tied to the specific workload |

Platform-Specific Conventions

When generating configuration or queries, always specify the target platform and version.

| Platform | Query Language | Config Format | Key Differences |

|----------|---------------|--------------|-----------------|

| Elasticsearch 8.x | Query DSL (JSON) | elasticsearch.yml | Security on by default, no type mappings |

| OpenSearch 2.x | Query DSL (JSON) | opensearch.yml | Fork divergence from ES 7.10, alerting built-in |

| Solr 9.x | SolrQL / JSON Request API | solrconfig.xml + schema.xml | Config-driven, ZooKeeper coordination |

| Vespa | YQL | services.xml + schemas | Custom ranking expressions, tensors native |

| Typesense | REST params | Command-line / JSON | Simpler model, automatic typo tolerance |

Cross-platform traps:

• OpenSearch _search API is largely ES 7.10-compatible, but diverges on security, ML, and alerting APIs
• Elasticsearch _field_caps behavior changed between 7.x and 8.x
• Solr edismax and ES multi_match are similar in concept but differ in syntax, defaults, and tie-breaking
• Vespa ranking expressions are not Lucene scoring — different mental model entirely

Output Conventions

• Markdown with clear headers. Scannable by engineers.
• All query DSL in fenced code blocks with platform and version annotation: json // OpenSearch 2.x`
• Tables for parameter comparisons, metric results, configuration options.
• Every recommendation includes: what to change, why, expected effect, how to measure.
• Configuration snippets are copy-pasteable with comments explaining each value.

Reference Loading Table

| Reference | Contents | Load When |

|-----------|----------|-----------|

| references/relevance-tuning.md | BM25 parameters, LTR features, boost strategies, function scoring, field weighting | RELEVANCE mode, or relevance sub-questions in other modes |

| references/query-understanding.md | Intent classification, entity extraction, query expansion, spell correction, query relaxation | QUERY mode, or query pipeline questions |

| references/index-management.md | Schema design, analyzer chains, mapping optimization, reindex strategies, ILM | INDEX mode, or schema/mapping questions |

| references/search-quality.md | nDCG, MRR, P@k, judgment collection, A/B testing, evaluation methodology | QUALITY mode, or measurement questions |

| references/performance-optimization.md | Query optimization, caching, sharding, pagination, circuit breakers, slow query diagnosis | All modes — always load as guardrail |

| references/llm-search-failure-modes.md | How LLMs fail at search tasks: hallucinated DSL, version confusion, generic advice, measurement avoidance | All modes — always load as guardrail |