Automating Leadership Assessment Using GenAI and LLM Operations

ddi · HR · 2024 · source Agentic: no · promotional bias: high · source-backed · extracted with prompt 0.5.4 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Leadership assessment automation

DDI needed to automate behavioral-simulation scoring so leadership candidates could receive reports faster than the 24-48 hour manual assessor workflow while preserving scoring quality and governed model operations.

Constraints:

Architecture

DDI used the Databricks Data Intelligence Platform to experiment with and deploy LLM-based behavioral simulation analysis. The team tried GPT-4 prompt engineering, few-shot learning, chain-of-thought prompting, and self-ask prompts; used Databricks Notebooks and DSPy for prompt optimization; instruction fine-tuned Llama3-8b; tracked and packaged models with MLflow; registered models and governed data through Unity Catalog; and deployed serverless model-serving endpoints for production workflows.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  behavioral-simulation-responses[Behavioral simulation responses] --> databricks-notebooks[Databricks notebooks]
  databricks-notebooks --> dspy-optimizer[DSPy optimizer]
  databricks-notebooks --> fine-tuned-llama3-model[Fine-tuned Llama3 model]
  dspy-optimizer --> mlflow-lifecycle-manager[MLflow lifecycle manager]
  fine-tuned-llama3-model --> mlflow-lifecycle-manager
  mlflow-lifecycle-manager --> unity-catalog-registry[Unity Catalog registry]
  unity-catalog-registry --> model-serving-endpoint[Model serving endpoint]
Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component Role Interacts with
Behavioral simulation responses Candidate submissions to be scored and reported. Databricks notebooks
Databricks notebooks Collaborative experimentation environment for prompts, fine-tuning, and model development. Behavioral simulation responses, DSPy optimizer, Fine-tuned Llama3 model
DSPy optimizer Optimizes prompts for recall improvement. Databricks notebooks, MLflow lifecycle manager
Fine-tuned Llama3 model Instruction-tuned Llama3-8b model for scoring behavioral simulations. Databricks notebooks, MLflow lifecycle manager
MLflow lifecycle manager Tracks experiments, artifacts, pyfunc models, traces, and automated GenAI evaluation. DSPy optimizer, Fine-tuned Llama3 model, Unity Catalog registry
Unity Catalog registry Provides model registration, metadata, lineage, and fine-grained access controls. MLflow lifecycle manager, Model serving endpoint
Model serving endpoint Deploys the scoring model with auto-scaling and serverless capabilities. Unity Catalog registry

Data flow

Candidate simulation responses enter Databricks experimentation workflows. Prompt-engineered or fine-tuned models classify/score the responses, DSPy optimizes prompts, MLflow tracks experiments and artifacts, Unity Catalog governs registered models/data, and Model Serving endpoints deliver automated reports in production.

4 supporting quotes
“Candidates complete an assessment and submit their responses. Trained human assessors then evaluate these submissions”article, stated
“DDI chose the Databricks Data Intelligence Platform to develop and deploy ML models for automating behavioral simulation analysis.”article, stated
“Prompt optimization with DSPy improved the recall score from 0.43 to 0.98. The instruction fine-tuned Llama3-8b achieved an F1 score of 0.”article, stated
“MLflow significantly aided in tracking experiments, logging artifacts as pyfunc models (i.e. [Python function models](https://docs.”article, stated

Why this architecture

DDI needed an end-to-end LLMOps platform rather than isolated prompt experiments. Databricks provided notebooks for experimentation, DSPy for optimization, MLflow for lifecycle and tracing, Unity Catalog for governance, and serving endpoints for production deployment. Prompt engineering handled quick adaptation, while instruction fine-tuning improved scoring performance.

3 supporting quotes
“Previous experiments with GenAI workloads — such as prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and pretraining — had been conducted”article, stated
“Previous experiments with GenAI workloads — such as prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and pretraining — had been conducted, but a more comprehensive, end-to-end solution was needed.”article, stated
“Databricks provided us with a dual advantage: the ability to build models and a repository to serve them from.”article, stated

Models used: OpenAI ChatGPT-4 · Llama3-8b · ML models for behavioral simulation scoring

Scale

Alternatives considered (roads not taken)

Continue trained human assessor scoring

Rejected because: Manual scoring took 24-48 hours and limited turnaround speed.

“Trained human assessors then evaluate these submissions, scoring them based on thorough analysis. Due to the depth of evaluation required and the process of inputting scores into our system, this typically takes 24 to 48 hours.”article, stated
“Due to the depth of evaluation required and the process of inputting scores into our system, this typically takes 24 to 48 hours.”article, stated

Prompt engineering alone

Rejected because: DDI also used DSPy prompt optimization and instruction fine-tuning to improve metrics.

“One approach DDI experimented with was few-shot learning, or prompt engineering an existing model with a limited number of examples to perform a new task.”article, stated
“Prompt optimization with DSPy improved the recall score from 0.43 to 0.98. The instruction fine-tuned Llama3-8b achieved an F1 score of 0.”article, stated

Standalone model experiments without an LLMOps platform

Rejected because: The company needed secure, cost-effective serving and lifecycle management.

“finding a partner capable of supporting the operations required for serving ML models securely, cost-effectively and easily.”article, stated

Patterns

prompt-versioning (prompting)

DDI experimented with few-shot, chain-of-thought, and self-ask prompt approaches for behavioral simulation analysis.

“One approach DDI experimented with was few-shot learning”article, stated
“chain of thought (COT) prompting, which structures prompts to mimic human reasoning”article, stated

fine-tuning-adaptation (modeling)

DDI instruction fine-tuned Llama3-8b and improved F1 over baseline.

“The instruction fine-tuned Llama3-8b achieved an F1 score of 0.86, compared to the baseline score of 0.76.”article, stated

eval-driven-development (evaluation)

Prompt optimization and fine-tuning were judged by recall and F1 improvements.

“Prompt optimization with DSPy improved the recall score from 0.43 to 0.98.”article, stated

observability-tracing (operations)

MLflow tracks experiments, artifacts, traces, and automated GenAI evaluation.

“MLflow significantly aided in tracking experiments, logging artifacts as pyfunc models”article, stated

privacy-preserving-processing (governance)

Unity Catalog and Azure AD/SCIM integration provide access controls and organized data management.

“a unified governance solution that provides fine-grained access controls, centralized metadata management and data lineage tracking.”article, stated

Tradeoffs

Automate scoring while keeping an enterprise LLMOps stack

Chose Databricks platform for experimentation, governance, and serving over standalone model scripts.

Rationale: DDI needed secure, scalable production serving plus lifecycle tracking and governance.

“supporting the operations required for serving ML models securely, cost-effectively and easily.”article, stated

Use both prompt optimization and fine-tuning

Chose DSPy plus instruction fine-tuned Llama3-8b over one prompt-engineered base model.

Rationale: Recall and F1 improvements came from complementary optimization paths.

“Prompt optimization with DSPy improved the recall score from 0.43 to 0.98.”article, stated
“The instruction fine-tuned Llama3-8b achieved an F1 score of 0.86”article, stated

Use serverless/auto-scaling serving

Chose Unity Catalog plus Model Serving endpoints over manually managed serving infrastructure.

Rationale: DDI wanted uninterrupted workflows and scalable model deployment.

“deploying models with auto-scaling and serverless computing capabilities.”article, stated

Gotchas (what bit them)

Prompt experiments alone were not enough for production.

Root cause: DDI had tried several GenAI workloads but still needed a comprehensive end-to-end operational solution.

Resolution: The company adopted Databricks for notebooks, MLflow, Unity Catalog, and serving endpoints.

“Previous experiments with GenAI workloads — such as prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and pretraining — had been conducted”article, stated
“a more comprehensive, end-to-end solution was needed.”article, stated

Behavioral simulations require adaptable reasoning prompts.

Root cause: Different scenarios require analysis of decision-making, problem-solving, and interpersonal responses.

Resolution: DDI experimented with few-shot, chain-of-thought, and self-ask prompting.

“different types of behavioral simulations.”article, stated
“self-ask prompts that allowed the model to generate questions and answers to better understand and process the simulations.”article, stated

Evaluation

DDI evaluated model quality using recall and F1 score changes, and evaluated business impact by report delivery time.

Metrics: recall score · F1 score · report delivery time · workflow continuity · model serving scalability

3 supporting quotes
“Prompt optimization with DSPy improved the recall score from 0.43 to 0.98.”article, stated
“The instruction fine-tuned Llama3-8b achieved an F1 score of 0.86, compared to the baseline score of 0.76.”article, stated
“reduced the simulation report delivery time from 48 hours to just 10 seconds.”article, stated

Outcomes

DDI serves more than 3 million leaders annually and used automation to reduce a 24-48 hour manual process to seconds.

“The switch to Databricks has significantly improved DDI's operations”article, stated
“leaders to receive immediate feedback, providing customer value that helps DDI scale its operations”article, stated