The About page expansively covers many of the drivers behind the work on this site. I’ve distilled a small number of key technical goals down to a conceptual framework entitled PROVEN. These are highly targeted at AI engineering. Here’s the concise bullet list expanding PROVEN into its six pillars:
- P | Post-training - Refining and adapting foundation models through fine-tuning, alignment, or distillation to specialize behavior for the prototype’s goals.
- R | Retrieval-Augmented Generation (RAG) - Grounding outputs in external knowledge sources—databases, documents, APIs—to ensure factuality and contextual relevance.
- O | Observability - Instrumenting AI systems with telemetry, tracing, and logging to expose and capture model inputs, outputs, and behavior across discrete runs.
- V | Velocity - Maintaining rapid iteration of development through automation, agentic tooling, and tight feedback loops that keep prototype progress measurable and adaptive.
- E | Evaluation - Applying quantitative metrics, qualitative review, and benchmark comparisons to assess performance, reliability, and regressions.
- N | Narration - Documenting the prototyping process through public artifacts – build notes, posts, papers, – to make the engineering transparent, reproducible, and open to critique.
Think of PROVEN as the charter of this publication — a mindful, systematic AI prototyping exploration that joins post-training and RAG for capability, observability and evaluation for rigor, velocity for rapid iteration, and narration for transparent, public reflection on the build process.