Prompt construction

Reduces noise and token usage by passing only relevant fields to the model.

Prompt engineering: designing templates that consume structured records with many optional fields

Prompt engineering, LLM application development, structured output generation, RAG pipelines

identify relevant fields → filter record → format selected fields into prompt template

• prompt template
• context window
• token budget
• field whitelist
• JSON schema
• function calling
• few-shot examples

• Pass entire record (simpler but wastes tokens and dilutes attention)
• summarize record first (adds latency and may lose signal)
• retrieval-augmented field selection (more complex but adaptive)

Including all fields by default — wastes tokens and dilutes model attention on relevant signals

Feature selection (ML preprocessing stage, not prompt-time), context pruning (broader scope covering retrieval and memory), schema validation (structural check, not semantic relevance)

Coming from SQL: may think SELECT * is acceptable — LLMs have hard context window limits and attention degrades with noise. Coming from REST API design: may pass entire objects by default — prompt templates need curated subsets to stay within budget.

• prompt template design
• context window management
• token budgeting
• schema-driven prompting
• few-shot example curation

When NOT to use: when the model genuinely needs full record context for accuracy (e.g., legal or medical reasoning). Performance: fewer fields directly cuts token cost and latency, often 40–80% savings on typical structured records. Boundary: some fields are conditionally relevant based on values in other fields, so static whitelists can miss context.

Cuts token costs by 40–80% on typical structured prompts and measurably improves extraction accuracy by removing distractor fields.

Field selection: like packing for a trip — bring only what you'll actually use, not everything in the closet.

Dynamic field selection driven by query intent and learned per-field importance scoring