Skip to main content
AI Fin Hub
Menu

Checklist, in AI in Markets

RAG for Filings Setup Checklist

Retrieval-augmented generation lets a model answer from the actual filing instead of training memory, but only if the retrieval and grounding are built correctly. This checklist covers setting up RAG over SEC filings and similar documents.

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

Your progress

Move item by item and keep your place

Progress saves locally, so you can work through the page over multiple sessions without resetting your checklist.

0/12 complete

Sections

Work in focused batches instead of one long wall

Section 1

Phase 1: Ingestion and chunking

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.

Section 2

Phase 2: Retrieval quality

3 items

Section 3

Phase 3: Grounding and verification

3 items

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 4

Phase 4: Security and cost

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.

Use the Financial Document Token Estimator: Paste a 10-K, 10-Q, 8-K or earnings transcript and see token count + one-pass extraction cost across ten frontier LLMs, with cache-hit toggle.

Tips

Small moves that make the checklist easier to finish

Most RAG failures are retrieval failures, not generation failures. If the right passage never reaches the model, no prompt engineering will produce a correct answer.
Tables are where naive chunking quietly breaks. A financial statement flattened into prose loses the structure that makes its numbers meaningful, so handle it on purpose.
A citation that does not support its claim is worse than no citation, because it manufactures false confidence. The faithfulness check, not the citation itself, is what earns trust.

Sources

  1. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , Lewis et al., NeurIPS (2020)
  2. OWASP Top 10 for Large Language Model Applications , OWASP Foundation (2023)

Keep the topic connected

  • Checklist LLM for Finance Deployment Checklist

    A pre-flight checklist for putting a large language model into a finance workflow: scoping, grounding, input security, numerical verification, and drift monitoring.

  • 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.

  • 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.

  • Checklist Prompt Design for Financial Extraction

    Financial extraction prompt checklist: enforce a strict schema, require source grounding, handle missing values, and test adversarial filings.