Skill Guide

Prompt engineering and LLM orchestration for claims reasoning and summarization

The systematic design of instructions and control flow for Large Language Models to extract structured reasoning, validate evidence, and generate accurate, auditable summaries of insurance, legal, or financial claims.

It directly reduces claims processing time and operational cost by automating complex reasoning tasks, while simultaneously improving consistency and compliance by enforcing standardized evaluation criteria at scale.

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How to Learn Prompt engineering and LLM orchestration for claims reasoning and summarization

1. Master prompt anatomy: Context, Instruction, Input Data, Output Indicator (CICO framework). 2. Understand core LLM parameters: temperature, top-p, max_tokens, and stop sequences. 3. Learn basic output formatting with JSON mode or XML tags for structured data extraction.

1. Implement few-shot prompting with domain-specific claim examples to guide model reasoning. 2. Design chain-of-thought prompts that force the model to list evidence, apply policy clauses, and then make a determination. 3. Avoid common pitfalls: ambiguous instructions, context window overflow, and hallucinated policy terms.

1. Architect multi-step orchestration pipelines: pre-processing (OCR/text extraction), primary reasoning (claim evaluation), post-processing (summary generation & audit trail). 2. Integrate retrieval-augmented generation (RAG) to ground model reasoning in live policy documents. 3. Develop evaluation metrics (e.g., faithfulness, completeness) and human-in-the-loop feedback systems for continuous prompt refinement.

Practice Projects

Beginner

Project

Auto-Extract Claim Data from a Single Narrative

Scenario

You are given a 500-word first notice of loss (FNOL) email. Extract key structured data points: claimant name, date of incident, policy number, and claimed amount.

How to Execute

1. Design a prompt with a clear system role ('You are an insurance claims data extractor'). 2. Provide a single, well-formatted example (one-shot) of input text and desired JSON output. 3. Use the OpenAI API or similar with `response_format: { type: "json_object" }` to enforce valid JSON. 4. Test with 5 variations of the FNOL email to ensure robustness.

Intermediate

Project

Build a Chain-of-Thought Adjudicator

Scenario

Process a property damage claim. The model must: 1) list the claimed items, 2) verify each against a provided policy excerpt (stored in a variable), 3) apply the deductible, 4) output a structured justification and final payable amount.

How to Execute

1. Structure the prompt into clear phases with delimiters (e.g., `### EVIDENCE EXTRACTION`, `### POLICY APPLICATION`). 2. In the prompt, explicitly instruct the model to output its step-by-step reasoning before the final answer. 3. Implement a function to dynamically inject the relevant policy clause into the prompt context. 4. Parse the model's final JSON output to separate the reasoning audit trail from the decision.

Advanced

Project

Orchestrate a RAG-Powered Claims Review Agent

Scenario

Create a system where an LLM reviews a complex liability claim by automatically retrieving relevant clauses from a vector database of 50,000 policy pages, applying case law principles, and generating a summary for a human adjuster with confidence scores.

How to Execute

1. Implement a retrieval step: use embeddings (e.g., text-embedding-3-small) and a vector DB (Pinecone, Weaviate) to fetch the top 3 most relevant policy sections. 2. Design a meta-prompt that synthesizes the retrieved context with the claim narrative, instructing the model to cite specific clauses (e.g., 'Section 4.2(a)'). 3. Add a final layer where the model rates its own confidence (High/Medium/Low) and flags areas requiring human review. 4. Build a feedback loop where adjuster corrections are logged and used to fine-tune embeddings or prompt templates.

Tools & Frameworks

Software & Platforms

OpenAI API (GPT-4, GPT-4o)LangChain / LlamaIndex (orchestration)Pinecone / Weaviate (Vector DB for RAG)PromptLayer / LangSmith (observability)

Use OpenAI for base model inference, LangChain to chain retrieval and reasoning steps, vector DBs to ground models in proprietary documents, and observability tools to track prompt performance and costs.

Methodologies & Frameworks

Chain-of-Thought (CoT) PromptingCICO (Context, Instruction, Input, Output) FrameworkRetrieval-Augmented Generation (RAG)Structured Output (JSON/XML) Modes

CoT forces explicit reasoning steps. CICO provides a reliable template for prompt design. RAG prevents hallucination by injecting real data. Structured output modes ensure parseable, machine-readable responses for downstream systems.

Interview Questions

Answer Strategy

Use the STAR (Situation, Task, Action, Result) framework adapted for technical design. Describe the pipeline stages: data extraction, evidence retrieval (RAG), reasoning (CoT), and summary generation. Emphasize the output structure (separate audit trail vs. executive summary). Sample answer: 'I'd implement a three-stage pipeline. First, a prompt extracts claimant and incident data into JSON. Second, a RAG prompt retrieves relevant policy clauses and prior case notes. Finally, a reasoning prompt uses CoT to evaluate liability, citing each source, and generates two outputs: a bullet-point audit log for compliance and a one-paragraph narrative summary for the adjuster.'

Answer Strategy

Tests debugging skills and understanding of LLM failure modes. Root causes often include ambiguous prompts, lack of domain context, or temperature settings too high. Sample answer: 'In a medical claims summarizer, the model hallucinated diagnosis codes. The root cause was the prompt lacked explicit instructions to use only ICD-10 codes from the provided medical report. I fixed it by adding a strict constraint in the system message ('Only use codes explicitly stated in the document') and lowered the temperature to 0.2 for deterministic extraction. I also added a validation step that cross-referenced extracted codes against a standard ICD-10 list.'