AI Context Engineer

AI Context Engineering  for B2B SaaS GTM

By David H. Deans

What is AI Context Engineering?

Context Engineering is the art and science of deliberately curating and managing all the information (the context) fed into an AI Large Language Model (LLM) window to achieve a specific, desired output.

The LLM will base its response on the information available in that prompt window. Context Engineering is the discipline of designing the dynamic systems and strategies that load the right information — at the right time, in the right format — into this limited working memory before the model generates a response.

This skill represents the natural evolution of Prompt Engineering, moving beyond crafting a single, perfect instruction to architecting an entire agentic information ecosystem around the AI model.

Why is Context Engineering Crucial?

Artificial Intelligence LLMs are incredibly powerful, but they operate under critical constraints that make context engineering essential to meaningful and substantive value creation:

• Every LLM has a finite context window — a maximum number of tokens (words/parts of words) it can process at one time. Once this limit is reached, older information is typically truncated, leading to forgetting. Context engineering ensures the most relevant data stays within the limit.

• While LLMs are trained on vast datasets, they don't have access to real-time, proprietary, or specific personal information. Context engineering enables you to inject these external facts directly into the model's awareness.

• When an LLM lacks necessary facts, it can hallucinate (generate plausible but false information). Providing grounded, accurate context significantly reduces this tendency.

• In multi-turn conversations or long-running tasks, the model needs to maintain a consistent persona and focus on the task's history. Context management strategies ensure this continuity.

The Components of AI Context

The "context" is a layered payload of information passed to the LLM, which can be broken down into the following key elements:

System Instructions: High-level, static rules that define the model's persona, behavior, and constraints. For example, "You are a product marketing manager," or "Always respond in a structured format."

Core Prompt/Query: The immediate user input or question the model is asked to address.  For example, "What are the best B2B SaaS GTM strategies for the U.S. market?"

Chat History/Memory: Short-term memory of the current conversation, which is critical for continuity. The last few user and assistant messages.

External Knowledge (RAG): Retrieved facts or documents from a knowledge base, often using Retrieval-Augmented Generation (RAG). Snippets from vendor assets, product documentation, or real-time data.

Tool Definitions & Output: Descriptions of external functions the model can call (e.g., an ROI calculator or an API) and the results of those calls. For example, Input: "get current subscription price." Output: "The current price is $45 per user."

Few-Shot Examples: Examples of the desired input/output pairs to demonstrate the required format or style. Input: "Summarize this vendor article." Output: "A 3-point bullet summary in context."

Core AI Context Engineering Techniques

Context engineering involves a systematic approach to managing the AI tool context throughout a software application's lifecycle:

Context Retrieval (Getting the Facts)

The process of finding and collecting the most relevant information from a vast data repository.

Retrieval-Augmented Generation (RAG): The most common technique. It involves indexing external documents, using the user's query to retrieve the most semantically similar chunks of text, and injecting those chunks into the LLM's context.

Context Processing (Optimizing the Payload)

Once retrieved, information must be optimized to be clear, concise, and fit the context window.

Compaction/Summarization: Using the LLM itself or other methods to distill long chat histories or document snippets into a shorter, high-signal summary.

Structuring and Formatting: Using delimiters, XML tags, or JSON schemas to clearly delineate different sections of the context (e.g., `<INSTRUCTIONS>`, `<FACTS>`, `<HISTORY>`) so the LLM model can process them more effectively.

Context Management (Handling Long-Term Tasks)

This involves maintaining coherence across many interactions that exceed the context window.

Long-Term Memory: Storing key facts, user preferences, and high-level summaries from past sessions in a persistent database (e.g., a Vector Store) for later retrieval.

Context Trimming: Implementing logic (e.g., a "rolling window" or simple truncation of old messages) to ensure the current context window remains under the token limit.

In Summary: Why it Matters

AI Context Engineering is a vital skill for building complex, reliable, and grounded GenAI applications.

By treating the context window as a deliberately engineered workspace, vendor product marketers can significantly enhance the capabilities, reliability, and precision of their GenAI application development.

Imagine the possibilities, explore the benefits

Contact Us today to learn more about how GenAI Context design can help you grow your B2B SaaS business. Schedule a free initial consultation. Discover how we enable you to reach and engage decision-makers.

Plus, learn how Generative AI is enabling B2B SaaS providers to drive net-new revenue growth.