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Search · Version 1.6.0 · Reviewed 2026-08-02

Autocomplete System Designer

Make a defensible decision about prefix index design and latency budgeting with evidence, explicit trade-offs, and a verification plan.

4 method steps 4 documented failure modes 4 diagnostic checks 7 quality gates

Designs low-latency autocomplete with prefix indexes, typo tolerance, popularity, personalization, freshness, abuse controls, and safe suggestion filtering.

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What this skill helps you do

  • Prefix index design
  • Latency budgeting
  • Suggestion safety

How Autocomplete System Designer works

You provide

Index config, analyzers, and queries that return the wrong thing

It inspects

Recall before ranking for prefix index design

It decides

A latency budgeting change scoped to the failing query class

You verify

Recall@k and NDCG re-measured on a judged query set

What it checks first

Autocomplete System Designer designs low-latency autocomplete with prefix indexes, typo tolerance, popularity, personalization, freshness, abuse controls, and safe suggestion filtering. Use it when the work involves Prefix index design, Latency budgeting, Suggestion safety.

  1. Whether relevance failures are recall problems or ranking problems, since the fixes are unrelated.
  2. Query analysis and index analysis symmetry, because a mismatch silently breaks exact matching.
  3. Whether rare tokens such as identifiers and codes are preserved rather than stemmed away.
  4. Click and abandonment data, which reveals relevance failures no offline metric captures.

Failure modes it recognizes

  • Aggressive stemming or lowercasing destroying exact matches on part numbers and error codes.
  • A relevance tweak that improves head queries while breaking the long tail nobody measures.
  • Synonym expansion introducing false positives that outrank the exact match.
  • Analyzer changes applied at query time but not reindexed, so the two sides disagree.

Answers it will reject

  • Tuning ranking weights before confirming the correct document is retrieved at all.
  • Judging relevance from a handful of favorite queries rather than a sampled distribution.
  • Boosting recency or popularity globally, which buries precise matches for specific queries.

Decision rules it applies

  • Fix recall before ranking; a document that is not retrieved cannot be ranked.
  • Keep query-time and index-time analysis identical, and reindex when either changes.
  • Evaluate on a judged set that includes head, torso, and tail queries.

Evidence it asks for

  • Measure recall@k and NDCG on a labeled query set before and after each change.
  • Track zero-result and reformulation rates as leading indicators of relevance failure.
  • Inspect the analyzed form of both the query and the document when a match fails.

The method inside

  1. Turn prefix index design into explicit functional requirements and quality-attribute constraints.
  2. Model the critical path, state, trust, and failure boundaries that govern latency budgeting.
  3. Compare viable designs for suggestion safety against weighted constraints and operational ownership.
  4. Select a design with consequences, rollout stages, observability, and a reversible adoption path.

Deliverables

  • Prefix index design assessment
  • Latency budgeting decision and action plan
  • Suggestion safety verification checklist

Evidence requirements

  • Functional and quality requirements
  • Scale, latency, consistency, cost, and compliance constraints
  • Current topology and alternatives considered

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Design autocomplete for ten million product names with typo tolerance and suggestions under fifty milliseconds.

Expected output

Use a memory-resident prefix structure for head queries, fuzzy candidates only after a minimum prefix, and precomputed popularity by locale. Keep personalization as a rerank layer so the shared candidate cache remains effective...

Boundaries and compatibility

Ideal for

  • Prefix index design: produce a decision or artifact grounded in supplied evidence.
  • Latency budgeting: produce a decision or artifact grounded in supplied evidence.
  • Suggestion safety: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Producing a generic reference architecture without requirements
  • Hiding material trade-offs behind best-practice language

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.