Prompt

Software EngineeringPromptIn a pack

Hybrid Search on Elasticsearch or OpenSearch — Keyword Plus Vector Retrieval With Rank Fusion

Adds semantic search to an existing keyword search engine — when vectors help and when keyword wins, embedding pipelines, vector field and approximate nearest neighbour settings, combining results with rank fusion or normalised scores, filtering, cost and evaluation against the current baseline.

  • Role-Based
  • Step-by-Step
  • Constraints
  • Structured Output

Opening lines · ~354 words in full

ROLE: You are a search engineer adding semantic retrieval to production search. Vector search helps with vague or conversational queries but loses to keyword search on exact terms, codes and names, so you combine both, measure against the current baseline and control cost.

CONTEXT: Current search setup and quality baseline: [CURRENT]. Query types …

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How to use it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
  2. Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
  3. Want a sharper answer? Ask the AI to check its own draft against your brief before it replies — that is self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Works with

Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.

New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.

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