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Challenge

Build Startup IPO Regulatory Auditor with LlamaIndex Advanced RAG

Analyzing complex IPO draft prospectuses (DRHP) requires high-precision semantic retrieval across hundreds of financial pages. Build a advanced RAG query engine using LlamaIndex to query regulatory filings and extract key financial risks with sub-second retrieval accuracy.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an IPO prospectus regulatory auditing agent using LlamaIndex advanced RAG and hierarchical vector indexing.

Delivery guide

How work is evaluated

Evaluation

Evaluates retrieval recall and factual correctness of risk analysis extracted from startup IPO filings.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

chunk_attribution_present

Ensures retrieved answer cites specific page node source chunk

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

faithfulness

Factual consistency of answer relative to source DRHP text • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Build hierarchical document index trees with LlamaIndex

  • Implement hybrid vector and keyword search retrievers for financial filings

  • Automate key risk factor extraction from SEBI DRHP filing prospectuses

Resources and assets

Reference links and supporting material

Dataset notes

Text extracts and tables from SEBI draft red herring prospectuses (DRHP) filed in 2026.

How this agent runs

Evaluates retrieval recall and factual correctness of risk analysis extracted from startup IPO filings.

Challenge input

JSON containing company_name and target_metric string

LlamaIndex

Provides structured document indexing and multi-document RAG capabilities

Evaluated output

JSON containing answer, source_chunk_id, and faithfulness_score

Checks for
  • Ensures retrieved answer cites specific page node source chunk
  • Factual consistency of answer relative to source DRHP text • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: DRHP-RAG-Bench
  • Faithfulness target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • Exa · Optional
Observation
  • Braintrust · Optional
Reward / Eval
  • Braintrust · Optional

Evaluation contract

  • chunk_attribution_present · Weight 1
  • faithfulness · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Build Startup IPO Regulatory Auditor with LlamaIndex Advanced RAG