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Checklist, in AI in Markets

LLM for Finance Deployment Checklist

This checklist turns the deployment guide into a sign-off list. Work down it before going live and again after any model or prompt change. Items are tagged by priority: essential items gate the launch, recommended items prevent the most common production incidents, and nice-to-have items harden a working pipeline.

14 items. Published May 26, 2026. By AI Fin Hub Research.

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Sections

Work in focused batches instead of one long wall

Section 1

Phase 1: Scope and model selection

3 items

Use the Model Selector for Finance: Input task, latency budget, cost budget, context size, and quality sensitivity; get ranked model recommendations with rationale — grounded in published.

Use the Token-Cost Optimizer: Compute the dollar cost of a trading research loop across Claude, GPT, and Gemini. Prompt length × model × retry × call volume → cost per idea and per.

Section 2

Phase 2: Grounding and retrieval

3 items

Use the SEC Filing Chunk Optimizer: Pick a filing archetype, tune chunk size and overlap, and see chunk count, embedding cost, and structural-boundary warnings across three chunking strategies.

Use the Hallucination Detector: Paste a source document + an LLM's extraction. Every numeric claim in the output is checked against the source. Client-side. Catches silent fabrication.

Section 3

Phase 3: Security and numerical integrity

3 items

Use the Prompt Injection Tester: Red-team a finance agent against 23 documented prompt-injection attacks — direct override, role confusion, indirect injection via retrieved content.

Section 4

Phase 4: Regression and monitoring

5 items

Use the Prompt Regression Tester: Run the same prompt against multiple models (Claude 4.5/4.6/4.7, GPT-5, Gemini 2.5) with your own keys. Diff outputs, score drift, catch regressions.

Tips

Small moves that make the checklist easier to finish

Pin the model version explicitly. Providers change behavior between versions, and an unpinned deployment can degrade overnight without a single line of your code changing.
Sample production outputs for human review continuously, not just at launch. Drift shows up in the tails first, where aggregate metrics are slowest to move.
Treat the prompt and the verification engine as the real product. The model is a swappable component; the checks around it are the asset that makes the pipeline trustworthy.

Sources

  1. OWASP Top 10 for Large Language Model Applications , OWASP Foundation (2023)
  2. Artificial intelligence in UK financial services 2024 , Bank of England and Financial Conduct Authority (2024)

Keep the topic connected

  • Guide How to Deploy an LLM in a Finance Pipeline

    A practical guide to putting a large language model into a finance workflow safely: scoping the task, grounding answers, guarding inputs, and verifying every number.

  • Learn Prompt Injection

    Prompt injection: when untrusted text in a prompt overrides system instructions. The attack patterns and the structural defenses that work in production.

  • Learn LLM Hallucination Detection in Finance

    How to detect LLM hallucinations in financial outputs: citation grounding, verifiable-claim checks, and cross-model agreement that flag fabricated data.