Context Engineering: Why Context is the Real AI Differentiator

One factor stands out for AI success and that is context. While technical capabilities like model performance, infrastructure, and data quality get most of the attention, context engineering remains a quiet force multiplier.

Done well, context creates high-value AI interactions. 

Context is everything in the knowledge world

Having worked in the knowledge management field for decades, context is key to knowledge. You can have the best search tools or the largest repositories of information, but without rich, well-engineered context, knowledge remains fragmented and often underused.

Context is the lens that transforms isolated data points into actionable knowledge. It refers to the background information, relationships, and circumstances that give meaning and relevance to facts, documents, and experiences.

Knowledge becomes truly valuable when it is contextualized. This is when each piece of information is clearly categorized, connected, and enriched with the "why" and "how," not just the "what." At the heart of every effective knowledge management strategy lies the ability to capture, structure, and tag content with rich context, ensuring that it can be retrieved and applied accurately when it matters most.

Generative AI (GenA AI) is no different. Its intelligence is bounded by the context it can access and understand. Without well-engineered context, even the most advanced models will guess, hallucinate, or miss the nuance that human experts rely on. With strong context engineering, AI becomes not just a retrieval tool, but a reasoning partner—delivering insight that is relevant, precise, and trusted.

What is context engineering?

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 vs 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.

Using Gen AI without context

Without strong context, Gen AI systems frequently suffer from failures, often in unpredictable ways:

• Hallucinations: models generate believable but false information.
• Incoherence: responses shift unpredictably as models lose track of prior inputs.
• Low trust: users disengage when outputs are or feel random or wrong.

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 that backs up context

Recent MIT research highlights that effective AI relies on providing “sufficient context", not just more information, but the right information at the right moment which significantly enhances LLM performance across applications.

Gartner forecasts that by 2029, agentic AI will independently resolve 80% of routine customer service queries, driving substantial operational efficiencies. Context engineering is essential to success in agentic AI systems. It addresses and lowers high failure rates which is often around 60% to 90% in complex multiagent systems (MAS) that arise from interagent misalignment and coordination breakdowns, according to industry studies.

How context improves Gen AI

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:

• Content generation: Models generate more coherent narratives when given structured themes and business tone guidelines.
• Customer support: AI agents resolve queries faster when they understand user history and current product context.
• Creative work: Context around genre, style, or previous outputs allows for generative design that feels intentional, not random.

Key components of context engineering

1. Knowledge architecture (i.e. Information Architecture)

• Enterprise knowledge is captured, tagged, and linked across systems. This ensures AI doesn’t just search, it understands contextually relevant information at scale.
• Content hierarchies that define relationships between concepts and documents
• Controlled vocabularies, glossaries, and ontologies to standardize language
• Metadata schemas that enable fine-grained filtering and retrieval
• Governance models to ensure data accuracy, versioning, and lifecycle management

2. Domain adaptation

• Govern large models' behaviour through curated domain knowledge collections
• Incorporating structured ontologies and taxonomies specific to the field
• Using system prompting to shape behavior

3. Persona context

• Role-based access and filters to surface the right content through specific Knowledge Collections
• Adaptive prompts that reflect user preferences and goals
• Multi-turn memory to track conversation history and intent evolution

4. Retrieval-Augmented Generation (RAG)

• Fine-grained chunking strategies preserving sentence-level meaning
• Source mapping to ensure every AI claim links to evidence
• Detailed vector embeddings optimized for detail
• Multi-source retrieval that combines static knowledge with real-time data

5. Human-in-the-loop feedback

• Feedback tagging or feedback mechanism from users at point-of-use
• Automated monitoring of AI output quality and drift
• Iterative adjustments to retrieval and grounding mechanisms

6. Temporal awareness (time context)

• Timestamped data and version control in the knowledge base
• Freshness scoring in retrieval pipelines
• Temporal reasoning models that interpret date-based relevance (e.g., precedence in law, market trends in finance)

7. Data quality and governance (the cross-cutting Layer)

• Reducing data deduplication, improving validation, and enrichment
• Unified taxonomies to break down silos
• Data observability to detect anomalies, drift, or missing information

Use cases for context engineering

1. AI-powered legal research

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.

2. AI in contact centers

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.

3. AI-driven product recommendations

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 engineering and Agentic AI

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

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.

Context Engineering research papers

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.