ForgeBot Agent: RAG Pipeline Implementation Plan

In this episode, we explore a practical overhaul of ForgeBot’s solution generation pipeline, guided by a detailed document on integrating advanced AI tools. We break down a workflow split into modular stages—solution creation, evaluation, and iterative refinement—using Jira API calls to fetch task data and retrieval-augmented generation to ground solutions in solid context. We dive into how LLM-as-judge evaluations score solution quality, with self-improvement loops refining outputs over time. We also cover multi-model orchestration, assigning tasks to models based on their strengths—like one for fast text generation, another for heavy reasoning—to keep costs and compute in check. It’s a no-nonsense look at building a maintainable system that autonomously tackles Jira tickets using prompt engineering, structured outputs, and modular services, cutting down on human hand-holding.