One thing that becomes very clear once you build this on AWS is that context engineering stops being a prompt-writing exercise and turns into a system design problem.
In production Bedrock setups, most quality issues come from how context is assembled, summarized, and re-injected across steps rather than from model choice or wording. We’ve seen “hallucinations” disappear once context flow, retrieval timing, and compaction were made explicit in the architecture.
This article connects well with a direction I’m starting to explore in Build with AWS: treating GenAI systems as distributed systems, where state, cost, and failure modes matter as much as prompts.
Playbook
Augment legacy plan user is here . I loved augment.
One thing that becomes very clear once you build this on AWS is that context engineering stops being a prompt-writing exercise and turns into a system design problem.
In production Bedrock setups, most quality issues come from how context is assembled, summarized, and re-injected across steps rather than from model choice or wording. We’ve seen “hallucinations” disappear once context flow, retrieval timing, and compaction were made explicit in the architecture.
This article connects well with a direction I’m starting to explore in Build with AWS: treating GenAI systems as distributed systems, where state, cost, and failure modes matter as much as prompts.