We’ve seen a recurring pattern in both successful and struggling AI deployments: context. More specifically, the presence or absence of context engineering, the disciplined, intentional design of context so AI can understand and act effectively in real-world settings.
While technical capabilities like model performance, infrastructure, and data quality get most of the attention, context engineering remains a quiet force multiplier or failure point. Done well, it aligns data, language, tools, and user environments to create high-value AI interactions. Done poorly, it leads to hallucination, poor adoption, and pilot fatigue.
Context engineering is the practice of structuring inputs and environments so that AI systems can interpret information accurately and perform effectively. It involves curating language, metadata, prompts, environmental signals, workflows, and user interfaces to “speak” to AI in a way that maximizes relevance and minimizes ambiguity.
Prompt engineering is a part of context engineering. Prompt engineering focuses on designing effective questions, while context engineering involves providing the AI with adequate background knowledge, data connections, and environmental information to deliver an informed response.
Without strong context, Gen AI systems frequently suffer from failures, often in unpredictable ways:
In most sectors, and certainly in law, healthcare, and finance, these failures are unacceptable.
Unlike AI tools that operate on narrow, structured tasks, such as many Machine Learning (ML) based AI tools, the LLMs being Gen AI are generalist models and the Gen AI performance hinges on how well you constrain, enrich and direct them. The impact – with Gen AI going mainstream - is that context engineering has moved from niche to necessary.
Research from Accenture highlights that only 12% of companies have reached a level of Gen AI maturity where they can effectively use their data to generate business value.
In Gen AI, context is everything the model sees before producing tokens: the prompt (including the user's question), relevant documents, environmental signals, and prior interactions. Strong context provides a boundary and enables the AI to stay grounded, relevant, accurate, and helpful.
Without it, even state-of-the-art models hallucinate, give generic responses, or veer off-topic. But when context is well designed and embedded, responses from the same models feel intelligent, efficient, and even humanlike.
Consider simple use cases like:
Research on this subject is limited. There are however a few themes that stand out across a few articles and research papers, along with our approach to context engineering.
Knowledge architecture (Information Architecture ++)
This is your foundation and how content is structured, labeled, and semantically organized. Think: content hierarchies, glossaries, taxonomies, and metadata.
Domain adaptation
This is the process of tailoring a general-purpose model to understand the nuances of specific industries or tasks. A legal AI, for instance, needs domain adaptation to correctly interpret legal terminology, precedent relationships, and jurisdictional differences.
Persona and role modeling
Different users mean different usage patterns. Understanding the impact on role-based prompts is important. Context engineering embeds these distinctions into the system through underlying prompt instructions, guardrails, and dynamic interfaces.
Human-in-the-loop feedback
Context engineering is not a one-off task. It’s an ongoing process of learning and refinement. By capturing where AI gets it wrong, and why, you can iteratively tune the underlying instructions, provide better prompt guidance, refine the grounded data, or consider workflows to improve relevance and reduce errors. Deloitte’s AI Readiness Framework underscores the need for robust feedback loops between users and systems to build trust and adoption.
Retrieval-Augmented Generation (RAG)
RAG remains a cornerstone of effective context engineering – and providing solid grounding. RAG's role is evolving, delivering narrative fragments rather than whole documents. That’s why well-engineered context includes source mapping, chunking strategies, and vector embeddings that preserve semantic integrity. Context engineering ensures that what's retrieved is both relevant and usable.
Use case: In legal firms, generative AI can draft documents or summarize cases. But without context engineering, it risks referencing irrelevant law (such as from another country or area of law) or outdated precedents.
How context engineering helps: Embedding domain-specific taxonomies, jurisdiction filters, and document metadata ensures that responses are relevant and trustworthy.
Use case: Customer service agents use AI to generate responses. Without context, AI may hallucinate or miss critical product nuances.
How context engineering helps: Real-time RAG pipelines with product catalogues, customer history, and intent classification provide high-context responses that reduce escalation and improve CSAT.
Use case: E-commerce systems suggest items, but without understanding the customer’s history or search intent, they miss the mark.
How context engineering helps: Context-engineering would use prior browsing behavior, time of day, location, and product affinities to tune suggestions dynamically.
Context contains the nuances and operating patterns of an organization. Context engineering is therefore also an important consideration for both agent-to-agent interactions (such as when one agent hands off to another) and overall agent workflows.
Atlas Fuse stands apart by systematically embedding context into every interaction. Atlas Fuse ensures that Gen AI has access to structured and relevant knowledge, relevant to the user’s role and in their daily flow of work – directly within the tools the user already uses.
At the core of Atlas Fuse is robust AI orchestration with transparent audit trails, role-based permissions, feedback mechanisms, domain specific metadata and enterprise-grade security.
Every insight is explainable. Every decision traceable. Every outcome more valuable.
Alegre, Unai & Augusto Wrede, Juan & Clark, Tony. (2016). Engineering Context-Aware Systems and Applications: A survey. Journal of Systems and Software. 117. 10.1016/j.jss.2016.02.010.
Liu, Pengfei, et al. "Pre-train Prompt for Programming: Towards Language Models as Software Developers." arXiv preprint arXiv:2102.07350 (2021).
Zhao, Wayne Xin, et al. "Calibrate Before Use: Improving Few-Shot Performance of Language Models." ICML 2021.
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