Gregor Schumacher, Sujay Joshy & Marcel Gocke
New AI-Centric SDLC: Rethinking Software Development with Knowledge Graphs
#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.
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How AI will reshape software development and documentation
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Using generative AI to enhance developer productivity
Throwing off the burdens of scale in engineering
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The current era of AI-assisted development
From Punch Cards to AI-assisted Development
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05:25 MIN
Applying AI across the entire software development lifecycle
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02:25 MIN
Applying AI beyond code generation in the SDLC
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