If you have both unique domain expertise and know how to make it usable to your AI systems, you’ll be hard to beat. The post Context Engineering as Your Competitive Edge appeared first on Towards Data Science. Today, I would like to zoom in on context engineering — the discipline of dynamically filling the context window of an AI model with information that maximizes its chances of success. Context engineering allows you to encode and pass on your existing expertise and domain knowledge to an AI system, and I believe it is an important component for strategic differentiation. If you have both unique domain expertise and know how to make it usable to your AI systems, you’ll be hard to beat.

One of the most critical factors for success is a tight handshake between domain experts and engineers. Domain experts are needed to encode domain knowledge and workflows, while engineers are responsible for knowledge representation, orchestration, and dynamic context construction. In this article, I will summarize the components of context engineering as well as the best practices that have established themselves over the past year. One of the most critical factors for success is a tight handshake between domain experts and engineers. Domain experts are needed to encode domain knowledge and workflows, while engineers are responsible for knowledge representation, orchestration, and dynamic context construction.

Knowledge about the domain and specific tasks turns a generic AI system into a domain expert. Tools allow the agent act in the real world. Memory allows the agent to personalize its actions and learn from user feedback. As the system matures, you will also find more and more interesting interdependencies between these three components, which can be addressed with proper orchestration. Let’s dive in and examine these components one by one. We will illustrate them using the example of an AI system that supports RevOps tasks such as weekly forecasts. Without this information, users will have to manually adjust the system’s outputs. They will explain that enterprise deals slip more often in Q4, correct expansion assumptions, and remind the model that discount approvals are currently delayed.

RAGIn Retrieval-Augmented Generation (RAG), company- and domain-specific knowledge is broken into manageable chunks. Each chunk is converted into a text embedding and stored in a database. Text embeddings represent the meaning of a text as a numerical vector. Semantically similar texts are neighbours in the embedding space, so the system can retrieve “relevant” information through similarity search. Now, when a forecasting request arrives, the system retrieves the most similar text chunks and includes them in the prompt. This is elegant, and every freshly baked B2B AI team that respects itself has a RAG initiative underway. However, most prototypes and MVPs struggle with adoption. The naive version of RAG makes several oversimplifying assumptions about the nature of enterprise knowledge. It uses isolated text fragments as a source of truth. It assumes that documents are internally consistent. It also strips the complex empirical concept of relevance down to similarity, which is much handier from the computational standpoint.

Articulating knowledge through graphsMany teams dump their available data into an embedding database without knowing what’s inside. This is a sure recipe for failure. You need to know the semantics of your data. Your knowledge representation should reflect the core objects, processes, and KPIs of the business in a way that is interpretable both by humans and by machines. For humans, this ensures maintainability and governance. For AI systems, it ensures retrievability and correct usage. The model must not only access information, but also understand which source is appropriate for which task. Graphs are a promising approach because they allow you to structure knowledge while preserving flexibility. Instead of treating knowledge as an archive of loosely connected documents, you model the core objects of your business and the relationships between them. Depending on what you need to encode, here are some graph types to consider: taxonomies or ontologies that define core business objects — deals, segments, accounts, reps — along with their properties and relationships.

ToolsForecasting is not analytical, but operational and interactive. Your Head of RevOps explains: “I’m constantly jumping between systems and conversations — checking the CRM, reconciling with finance, recalculating rollups, and following up with reps when something looks off. The whole process interactive. The context builder must manage three distinct resources: domain knowledge, tools, and memory. Each resource plays a unique role in transforming generic AI into a domain-specific powerhouse. By intentionally designing how these components interact, organizations can create systems that reflect their unique operational DNA. This isn’t just about better outputs — it’s about embedding your competitive edge into the very fabric of your AI systems.