Leveraging Large Language Models for Interview and Assessment Workflows

Overview

Large language models have opened new possibilities for automating knowledge work that previously required extensive human effort. One particularly promising application is in interview and assessment workflows, where organizations need to process large volumes of documents, extract structured information, and generate comparative analyses. Whether reviewing application materials, synthesizing interview notes, or analyzing policy documents for compliance, LLMs can dramatically accelerate these workflows while maintaining consistency.

This article shares practical experiences from experimenting with various LLM-based approaches for document processing in assessment contexts. The insights apply broadly to any scenario where you need to collect information from multiple sources, compress and structure that information, and answer analytical questions based on the aggregated data.

The Problem

Assessment and interview processes generate substantial documentation. Application materials arrive in varying formats with inconsistent terminology. Interview notes capture information in different styles depending on who conducted the interview. Policy documents that govern the process evolve over time and differ across departments. Making sense of this heterogeneous information traditionally requires significant human effort.

Consider the challenge of comparative analysis. When evaluating candidates or reviewing institutional policies, decision-makers need to understand not just individual cases but patterns across many cases. Which requirements appear consistently? Where do policies diverge? What trends emerge over time? Manually extracting and comparing this information across dozens or hundreds of documents is tedious and error-prone.

The promise of LLMs is automating much of this extraction and synthesis work. However, naive approaches often disappoint. Simply asking an LLM to search for and retrieve specific information frequently produces incomplete or inaccurate results. The models may hallucinate details, miss important nuances, or fail to handle domain-specific terminology correctly. Effective use of LLMs requires careful attention to prompt design and workflow architecture.

The Approach

Successful LLM integration in assessment workflows follows several key principles. The first is separating collection from analysis. Rather than asking an LLM to both find information and analyze it in a single step, break the workflow into stages. First use appropriate tools to collect relevant documents, then use LLMs to process and analyze the collected content.

The second principle is progressive summarization. Long documents should be compressed before being combined for comparative analysis. Asking an LLM to summarize a single document while preserving key details produces better results than feeding multiple complete documents into a single analysis prompt. The summarization prompt should explicitly specify what information must be retained.

The third principle is structured output formats. When extracting information for downstream processing, request specific output formats such as JSON or structured tables. This makes it easier to validate outputs and integrate them with other systems. Include examples of the desired format in the prompt to guide the model.

The fourth principle is iterative refinement. Complex analytical questions often require multiple rounds of interaction. An initial prompt might identify the main themes or categories, followed by targeted prompts that explore each category in depth. This multi-pass approach produces more thorough and accurate results than attempting everything in a single prompt.

Implementation

A practical workflow for document analysis might proceed as follows. First, documents are collected and preprocessed to remove extraneous formatting while preserving meaningful structure.

def analyze_documents(documents, analysis_questions):
    # Stage 1: Compress each document while preserving key information
    summaries = []
    for doc in documents:
        summary_prompt = f"""
        Summarize the following document, preserving all specific details about:
        - Dates and deadlines
        - Numerical requirements or quotas
        - Eligibility criteria
        - Required procedures or steps

        Document:
        {doc.content}
        """
        summary = call_llm(summary_prompt)
        summaries.append({
            "source": doc.name,
            "summary": summary
        })

    # Stage 2: Combine summaries for comparative analysis
    combined_context = format_summaries(summaries)

    # Stage 3: Answer analytical questions using combined context
    results = {}
    for question in analysis_questions:
        analysis_prompt = f"""
        Based on the following document summaries, answer this question:
        {question}

        If the information is not present in the summaries,
        indicate what is missing rather than speculating.

        Document Summaries:
        {combined_context}
        """
        results[question] = call_llm(analysis_prompt)

    return results

Several practical considerations improve reliability. Model selection matters significantly. More capable models handle nuanced questions and complex documents better, while smaller models may suffice for straightforward extraction tasks. Input length limits require attention: when documents exceed the context window, chunking strategies become necessary.

Validation is essential. LLM outputs should be spot-checked against source documents, particularly for numerical data and specific claims. Building in verification steps, such as asking the model to cite which source supports each claim, helps identify potential errors.

Temperature settings affect output consistency. For factual extraction tasks, lower temperature values produce more deterministic results. For creative synthesis or brainstorming applications, higher temperatures introduce useful variation.

The Value

Properly implemented LLM workflows can reduce document processing time by an order of magnitude while improving consistency. Tasks that previously required hours of careful reading and note-taking can be completed in minutes. This acceleration enables more thorough analysis: rather than sampling a subset of documents due to time constraints, organizations can process complete document sets.

The consistency benefits are equally important. Human reviewers inevitably vary in what they notice and how they interpret information. LLM-based extraction applies the same criteria uniformly across all documents, making comparative analysis more reliable.

These tools work best as augmentation rather than replacement for human judgment. The LLM handles the labor-intensive extraction and initial synthesis, surfacing relevant information in digestible form. Human experts then apply domain knowledge and contextual judgment to the synthesized information, making final decisions based on complete understanding rather than partial document review.

The lessons from document analysis extend to many assessment-adjacent workflows: generating structured interview guides, synthesizing feedback from multiple reviewers, identifying inconsistencies in policy documents, and preparing comparative briefings for decision-makers. As organizations become more sophisticated in their use of LLMs, these tools increasingly serve as a knowledge infrastructure layer supporting human expertise.