New AI-Centric SDLC: Rethinking Software Development with Knowledge Graphs

Why do large context windows fail for complex codebases? Learn how knowledge graphs provide the minimal, precise context AI needs to solve systemic engineering challenges.

#1about 1 minute

Rethinking productivity beyond just writing code faster

Software engineering productivity gains come from optimizing the entire process, as coding itself is only a small fraction of the total work.

#2about 3 minutes

Learning from failed large context window experiments

Attempts to refactor a large application by feeding the entire codebase into an LLM failed due to the inability to handle vast corporate context.

#3about 2 minutes

The inadequacy of vector databases for code

Vector databases are not ideal for codebases because similar code snippets and branches produce nearly identical embeddings, making it difficult to retrieve precise information.

#4about 4 minutes

Using knowledge graphs to model code structure

By representing code as a graph of interconnected nodes like classes and methods, it becomes possible to precisely query and retrieve specific call chains for LLM context.

#5about 1 minute

Creating a unified platform for corporate knowledge

A centralized platform was built to ingest code, documentation from tools like Jira and Confluence, and other data into a single, queryable knowledge graph.

#6about 4 minutes

Managing autonomous agents with graph-based systems

A knowledge orchestration platform provides agents with the right context, while a "knowledge graph of thought" audits their actions for reproducibility and control.