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Improve LLM reasoning for a domain task

CohereMachine Learning Engineer · Onsite
MediumMachine Learning EngineerOnsiteML System Design

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This problem tests your ability to design a robust pipeline for an LLM-based system that requires multi-step reasoning in a specialized domain (e.g., technical legal analysis or medical diagnosis).

Scenario

You are tasked with building a system for a legal tech company that automates the analysis of complex contract disputes. Users provide a contract text and a list of specific clauses, and the system must determine if a breach has occurred, citing specific sections of the contract and legal precedents.

Your model is currently struggling with 'hallucinated' interpretations and missing logical links in multi-step reasoning. Design an end-to-end system architecture to improve accuracy, reliability, and auditability.

Requirements

Your design should address:

  1. Data Strategy: How do you curate or synthesize training data to represent multi-step reasoning paths?
  2. Inference & Prompting: Should you use Chain-of-Thought, Tree-of-Thoughts, or Agentic workflows? How do you handle long-context windows versus retrieval-augmented generation (RAG)?
  3. Evaluation: How do you define a 'correct' reasoning path in a subjective domain? How do you measure the quality of the model's citations?
  4. Post-Training: When is it appropriate to use SFT (Supervised Fine-Tuning) versus RLHF/DPO for this specific reasoning task?
  5. Operations: How do you minimize latency while keeping the reasoning deep and verbose?

Hints

Hint 1

Consider how to incorporate 'Rationales' or 'Chain-of-Thought' traces into your ground-truth dataset to guide the model's intermediate logical steps.

Hint 2

Evaluate whether a two-stage approach—where an LLM first extracts key clauses and then reasons over them—would outperform an end-to-end generation.

Hint 3

Think about using 'Self-Consistency' or 'Verification' loops to check if the conclusion logically follows from the cited contract sections.

Follow-up questions

  • How would you handle a situation where the retrieved context is contradictory or noisy?
  • If the model consistently fails on edge cases in specific jurisdictions, what is your strategy to patch this without full model retraining?
  • How do you design a human-in-the-loop interface to ensure that AI-generated citations are verified by a subject matter expert before being presented to the user?