How to Write Better Prompts for Travel APIs and GPTs

Stop chasing flaky LLM outputs — get predictable itineraries, fares, and policies

Travel developers and product teams lose time and trust when LLMs produce inconsistent itineraries, wrong fares, or invented policy language. In 2026, travel stacks combine real-time fare APIs, NDC/ONE Order feeds, and domain-tuned LLMs — but without strong prompt design and QA guards you’ll still get “AI slop.” This guide gives you a practical, code-friendly playbook for writing prompts that reliably return validated itinerary, fare, and policy outputs.

The problem in 2026: more data, more surface for error

Late 2025 and early 2026 brought wider adoption of NDC channels, more frequent fare updates, and production LLMs with function-calling and schema validation features. Those advances let you automate complex flows — but they also increase the cost of hallucinations. Missing or wrong baggage rules, stale fares, or mis-parsed segment legs can mean lost revenue or customer complaints.

Key pain points teams face:

• LLMs produce plausible-looking but incorrect policy text.
• Itineraries have misordered segments or wrong carriers.
• Fare outputs miss taxes, use incorrect currency, or ignore fares’ TTL.
• Automation attempts fail without deterministic response formats.

Core principles: design prompts like production code

Prompt engineering for travel is software engineering. Treat prompts as versioned, tested, and monitored interfaces. Use these principles:

• Structure — separate context, task, schema, examples, and QA checks.
• Determinism — prefer low temperature, function calls, or model features that enforce structured output.
• Grounding — include fresh API data or retrieval results; never ask the model to invent dynamic facts.
• Validate — always validate model outputs against schemas and external APIs before actioning them.

Prompt anatomy: a reusable template for travel responses

Below is a modular prompt template you can adapt for itinerary, fare, and policy queries. Keep this template in source control and treat variations as feature branches.

Prompt Template (conceptual)

Include the sections in this order in your system+user messages or tool call.

1. System context — capability limits, safety, and role (e.g., "You are a travel data assistant. Do NOT invent prices or policy terms.").
2. Fresh data block — attach the exact API response JSON for the route/fare/policy being analyzed.
3. Task — explicit instruction (e.g., "Produce a normalized itinerary JSON and a fare breakdown JSON.").
4. Schema — JSON schema or function signature the model must obey.
5. Edge-case rules — explicit instructions about TTL, currencies, and fallbacks.
6. Quality checks — list validator checks the model must compute and return (e.g., "price_vs_taxes_check: true/false").
7. Examples — 1–2 golden examples showing valid input → valid output.

Concrete prompt example: normalized itinerary + fares

Use this as a baseline. Send the API response in the Fresh data block and request only JSON.

{
  "system": "You are a strict travel data assistant. Never invent prices, flight numbers, or dates. If data is missing, return null for the field and add an explanations[] entry.",
  "user": "Given the attached API JSON (fresh_api_response), produce two JSON objects: itinerary and fare_breakdown. Return ONLY a JSON document matching the schema. Do not add prose. Use the timezone of the origin airport for local times. If you cannot validate a field, set it to null and add an explanations item.",
  "attachments": {"fresh_api_response": {...}}
}

Then pair this with a JSON Schema or function call so the model cannot wander.

Example JSON Schema: itinerary (short)

{
  "type": "object",
  "properties": {
    "itinerary": {
      "type": "object",
      "properties": {
        "legs": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "departure": {"type": "string", "format": "date-time"},
              "arrival": {"type": "string", "format": "date-time"},
              "origin": {"type": "string"},
              "destination": {"type": "string"},
              "flight_number": {"type": "string"},
              "operating_carrier": {"type": "string"}
            },
            "required": ["departure","arrival","origin","destination"]
          }
        }
      },
      "required": ["legs"]
    },
    "fare_breakdown": {"type": "object"},
    "validations": {"type": "object"}
  },
  "required": ["itinerary","fare_breakdown","validations"]
}

QA guards: automated checks you must run on every output

LLMs should be part of a validation pipeline. These QA guards catch bad outputs before downstream systems or customers see them.

• Schema validation — JSON schema or function signature enforcement. Fail hard on type or missing keys.
• Sanity checks — time ordering (departure < arrival), matching number of legs, and valid IATA codes (3-letter airports, 2-letter carriers).
• Price reconciliation — ensure fare_total == sum(base_fare + taxes + fees) within a tiny tolerance; check currency codes and convert to canonical currency for comparisons.
• TTL and freshness — compare fare quote timestamps to your cache TTL. If stale, flag for reprice.
• Policy fidelity — verify that the policy text references explicit rules present in the source API (e.g., change_fee: numeric or "See carrier policy ID 12345").
• Cross-check with authoritative API — perform a light re-quote against the live fare API for high-value or auto-booked transactions.
• Confidence and provenance — require the model to emit a provenance block listing source paths from the attached API response that justify each field.

Sample validation pseudo-code (Node-style)

const validateResponse = (resp) => {
  assert(schemaValidate(resp));
  assert(resp.itinerary.legs.every(leg => new Date(leg.departure) < new Date(leg.arrival)));
  assert(resp.fare_breakdown.currency === 'USD'); // or do conversion
  const sum = resp.fare_breakdown.base + resp.fare_breakdown.taxes + resp.fare_breakdown.fees;
  assert(Math.abs(sum - resp.fare_breakdown.total) < 0.5);
  if (!resp.provenance || resp.provenance.length === 0) throw new Error('missing provenance');
};

Handling edge cases and human-in-the-loop

Not all outputs should be fully automated. Define thresholds where the system escalates to a human reviewer.

• Auto-approve if fare delta < 2% and validations pass.
• Human review if reprice required, currency mismatch, or policy ambiguity.
• High-value bookings (e.g., >$1,000 per passenger) should default to human sign-off unless previously verified.

Integration patterns: where to place LLMs in your travel stack

LLMs are powerful for normalization, explanation, and enrichment — not authoritative quoting. Use them in these roles:

• Normalization layer — map disparate API payloads into a canonical schema (itinerary/fare/policy).
• Policy summarizer — create customer-facing short summaries of long carrier rules, but always attach the original canonical text and a provenance tag.
• Diagnostics & debugging — automatically explain why a price changed or why an itinerary is invalid.
• Developer aids — generate test cases, stubs, and example responses to bootstrap QA.

Example flow: Reprice alert automation

1. Poll fare API for monitored routes and attach response to LLM prompt.
2. LLM normalizes fare and calculates a human-friendly summary.
3. Validator checks fare math, currency, and TTL.
4. If validations pass and price < target threshold, push an alert (email/push) with a re-quote button that triggers a live reprice against authoritative API.
5. If high-value or validation flags appear, create a ticket for human review.

Developer tips: reduce friction and improve reproducibility

• Version prompts — keep changes in VCS with clear migration notes.
• Use function-calls & JSON schemas — modern LLM APIs support structured outputs that make validation easier.
• Keep temperature low in production — use temperature 0–0.2 for deterministic outputs; reserve creative settings for internal exploration only.
• Attach raw API JSON — never rely on the model to remember dynamic facts; pass them as context attachments or via retrieval augmentation.
• Log everything — input prompt, model output, and validation results for auditing and incident analysis.
• Automated regression tests — store golden examples and run them in CI whenever prompts or models change.

Monitoring, metrics, and SLOs for LLM-assisted travel flows

Treat LLM-driven transformations like any other microservice. Define SLOs and track these metrics:

• Schema pass rate (goal > 99%).
• Price-reconciliation failure rate (goal < 0.5%).
• Human escalation rate and mean time to resolution.
• False-positive/negative alert rates for repricing flows.
• Latency percentiles — LLM validation should fit within user experience constraints.

Case study: Chase & Reprice bot for a mid-size OTA (hypothetical)

In 2025 a regional OTA built an LLM normalization layer to handle multiple GDS/NDC inputs. They applied these steps:

1. Defined canonical JSON schemas for itinerary, fare_breakdown, and policy_summary.
2. Used function-calling with strict schemas and a provenance requirement.
3. Implemented validation chain: schema → sanity checks → live reprice for auto-bookable fares.
4. Added a human-in-the-loop for policy summaries flagged as ambiguous.

Result: automated alerts doubled the number of captured flash fares while keeping customer disputes below 0.2% of automated transactions. The team measured a 30% reduction in manual reprice checks.

Advanced strategies for 2026

Leverage these emerging trends to future-proof your prompts and pipelines.

• Domain-tuned LLMs — fine-tune or retrain with airline, GDS, and regulation corpora to reduce hallucinations on policy language.
• Vector retrieval with short-term cache — store recent fare objects and retrieval keys in a vector DB so LLMs can ground outputs in recent quotes.
• Model ensembles — run two models: one for normalization, one for checks; reconcile differences programmatically.
• Tooling and orchestration — use agents that can call your fare APIs, do math, and then return validated JSON rather than asking the model to compute alone.
• Privacy-first LLMs — in regulated markets, run inference in a VPC or on-prem to keep PII in-house while still using structured prompts.

Common prompt anti-patterns and fixes

• Anti-pattern: Long unstructured prompts with policy text buried inside. Fix: attach the policy JSON separately and ask for a summary with provenance.
• Anti-pattern: Asking for human-readable prose as the only output. Fix: require both machine-readable JSON and a short human summary.
• Anti-pattern: Allowing free-text dates and times. Fix: request ISO-8601 and specify origin timezone rules.
• Anti-pattern: No follow-up validation step. Fix: build a validation pipeline and fail-safe escalation paths.

Practical checklist before you deploy

1. Version and test your prompt templates in CI with golden cases.
2. Attach raw API responses to every model call.
3. Use JSON schemas or function calls to force structured outputs.
4. Implement sanity checks for times, currencies, and fare math.
5. Define escalation thresholds and HITL workflows.
6. Monitor SLOs and log provenance for audits.

"Speed without structure becomes AI slop — protect your customers with validation, provenance, and clear escalation."

Final takeaways: prompts as resilient APIs

In 2026, prompt engineering for travel is not a craft exercise — it’s part of your production API design. Build prompts that are versioned, deterministic, and paired with strong QA guards. Use schemas, provenance, and automated validation to eliminate hallucinations and keep repricing and booking flows reliable. When you treat prompts like code, you reduce risk and unlock automation that saves money and time.

Next steps and call-to-action

Start by converting one of your existing LLM calls into the template above: attach the raw API JSON, enforce a schema, add price reconciliation, and add one human escalation rule. Measure the schema pass rate and price reconciliation failure rate for two weeks — you’ll instantly see where the model needs constraints.

If you’d like a ready-to-run starter kit (prompt templates, JSON schemas, CI test scripts) tailored to flight search and NDC responses, visit botflight.com/resources or contact our developer team for a demo. We can help you reduce AI slop and deploy deterministic LLM workflows for itinerary, fare, and policy automation.

Related Reading

• Cashtags to DOIs: Mapping Financial Tagging Ideas to Scholarly Identifiers
• Constructing a Low-Cost Hedge for Ford Exposure While Europe Strategy Is Rebalanced
• Boundaries for Content Creators: A Toolkit for Saying No to Burnout
• Where to Host Spoken-Word Music Content Now: Spotify, YouTube, or Newcomers?
• Relocation Allowances 101: Using Budgeting Apps to Manage Employee Moves