Adaptive LLM Screening Study

Case study

Challenge

Adaptive, LLM-driven screening is easy to prototype and hard to make reliable: model calls are expensive and non-deterministic, follow-ups need to adapt, and sensitive data must be handled carefully.

Technical approach

Kalebtec ran an internal study that separated expensive model invocations from deterministic output using checkpoint-based patterns, generated adaptive follow-ups, and kept structured output privacy-conscious — validated end to end with a Rust API and a Next.js frontend.

AI & infrastructure decisions

Checkpoints so a model call is made only when it changes the outcome; deterministic assembly of results around those calls; and structured, privacy-conscious output rather than free-form generation.

Results

A validated pattern for adaptive LLM workflows that is cheaper to run and easier to reason about than a naive prompt loop.

Business impact

Internal research that sharpens how Kalebtec applies AI for clients: measured, structured, and cost-aware rather than demo-grade.

Overview

An internal Kalebtec study into adaptive, LLM-backed screening workflows — how to make model-driven profiling reliable, cost-aware, and privacy-conscious rather than a fragile prompt loop.

What We Explored

Checkpoint-Based Pipelines

Separated expensive, non-deterministic model calls from deterministic output assembly using checkpoint patterns, so a model is invoked only when it actually changes the result.

Adaptive, Structured Output

Generated dynamic follow-ups and handled structured output in a privacy-conscious way, validated end to end with a Rust (Axum) API and a Next.js frontend.

Delivery Focus

Reliability and cost-awareness over demo-grade prompting
Checkpointed model invocation with deterministic assembly
Structured, privacy-conscious output handling