Effective context engineering requires a modernized, unified data infrastructure along with retrieval and memory technologies like retrieval-augmented generation (RAG) and vector databases. It also demands strategic prioritization of information to identify critical data, determine exclusions, and apply appropriate information types contextually.
“Minimum context, accurate and up-to-date data, and machine-readable information are essential for successful context engineering,” Adil emphasizes.
3. Build AI governance and LLM observability from the outset
Implementing robust AI governance and LLM observability early ensures organizations maintain control over data utilization, monitor system efficiency, and proactively address risks. Without clear controls over retrieval processes, workflows, and model deployment, AI systems often process excessive data, inflating operational costs through increased token usage and API fees.
Adil highlights that many governance frameworks remain underdeveloped, particularly in security, cost management, project oversight, data protection, and architectural design. For AI systems to be transparent, compliant, trustworthy, and cost-effective, governance structures must be embedded into architectural designs, workflows, and decision-making processes from the beginning.
Early governance enables comprehensive LLM observability and benchmarking. By evaluating accuracy, utility, and adoption trends, teams can refine systems over time. Observability also fosters stakeholder trust through transparency into model performance, behavior, and failure points.
