Observed Signal · Sep 15, 2026 · Corporate Update · Source: JWP, Inc. · Impact: 3/5
JWX Launches New Blog Post: 'We Never Trained DJ'
JWX AI Engineering Manager Jay Krivanek explains that DJ, JWX's internal AI assistant, has never been trained, fine-tuned, or given an embedding index. Instead, DJ is a context-engineering harness built around frozen Claude models on AWS Bedrock, drawing live information from about 25 tool modules. The article distinguishes training from context engineering and argues that fine-tuning on new knowledge can increase hallucination, citing 37% accuracy on new facts in commercial fine-tuning APIs.
This post details a production-grade enterprise AI architecture that avoids model training entirely, using live context engineering and tool integration instead. It matters to the ad tech and mar tech ecosystem because it shows a cost-effective, low-hallucination pattern for building internal AI assistants.
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Key Takeaways & Evidence Grounding
- The blog post was published on September 15, 2026, and written by Jay Krivanek, AI Engineering Manager at JWX.
- DJ, JWX's internal AI assistant, has never been trained, fine-tuned, or built with an embedding index.
- DJ runs as a harness around frozen Claude models on AWS Bedrock with about 25 live tool modules, including GitLab, Datadog, JIRA, Buildkite, Sentry, Notion, Google Drive, Slack, Snowflake, and deployment tooling.
- The term 'context engineering' emerged in mid-2025 and is defined as providing the right information and tools, in the right format, at the right time.
- The article cites research that fine-tuning on new knowledge increases hallucination, and one benchmark showed commercial fine-tuning APIs averaged 37% accuracy on new facts.
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