AI Engineering¶

August 22, 2023
in Knowledge Strategy, AI Engineering, AI Governance
3 min read

Retrieval is a knowledge-governance problem

International Business Machines (IBM) Research has published a clear explanation of retrieval-augmented generation, an approach that gives a large language model access to external sources at the time of a request. Instead of relying only on patterns encoded during training, the system retrieves relevant material and uses it to generate a more current, domain-specific, and potentially verifiable answer.

Retrieval-augmented generation (RAG) is a promising architecture. It is not a substitute for governing the knowledge being retrieved.

June 27, 2023
in Product Management, AI Engineering, Human-Centered AI
3 min read

The AI product is more than the model

Microsoft has spent the spring adding generative artificial intelligence capabilities across Azure, developer tools, and business applications. In this month's platform update, one sentence deserves more attention than another feature list: generative AI does not absolve builders from thinking about what good product-making looks like.

That should be the operating principle for the current wave of experimentation.

May 18, 2023
in AI Engineering, Human-Centered AI, Platform Engineering
3 min read

AI performance is a workflow requirement

International Business Machines (IBM) and Intel have reported a substantial throughput improvement for natural-language processing tasks after integrating software optimizations with newer processors. The engineering result is a reminder that artificial intelligence performance comes from a stack: model, library, compiler, hardware, deployment environment, and workload.

For product teams, one more layer belongs in that stack—the human workflow.