Understanding context engineering for generative AI
With the foundation model selected, the real experimentation begins. This is where abstract business requirements transform into concrete interactions with the models. The journey from basic prompts to sophisticated context engineering is rarely linear. It's an iterative discovery process where each experiment reveals new insights into what the model needs to perform effectively.
Most PoCs start in a simple, experimental environment, such as a Jupyter notebook
or an Amazon Bedrock chat playground. The initial step is to translate the business
problem into a direct instruction for the LLM. This is basic prompt engineering. For
instance, if the business problem is that a support team spends too much time
summarizing long customer emails, the initial prompt might be a zero-shot
instruction. For example, it might be "Summarize the following email into three
bullet points." The team would test this prompt with a few real emails to see
if the LLM understands the fundamental task. This process is iterative. Each cycle
is informed by gap analysis, where you identify where the model falls short and then
refine the prompt or evaluation criteria accordingly. For more information about
prompt engineering, see Prompting best practices for
Amazon Nova understanding models (Amazon Nova documentation) and Prompt engineering techniques and best practices: Learn by doing with
Anthropic's Claude 3 on Amazon Bedrock
Teams often discover that simple instructions are insufficient for real-world
business problems. The LLM might produce generic summaries, miss key domain-specific
terms (such as product names or internal jargon), or lack crucial context from the
customer's history. This is a critical learning moment in the PoC: the quality of
the output is fundamentally limited by the quality of the input. This realization
drives the need for a more sophisticated strategy. It necessitates moving from
prompt engineering to the broader discipline of context engineering.
Context engineering is the practice of designing systems
that dynamically assemble the optimal set of information for an LLM to perform a
given task. The decision to adopt a more complex context engineering strategy is a
direct result of testing the initial, simpler prompts and finding them inadequate to
solve the business problem to the required standard. For more information about
context engineering, see Key
components of a data-driven agentic AI application
Context engineering treats the context window not as a static input field, but as a dynamic workspace that is populated with precisely the right components. While prompt engineering focuses on what to ask, context engineering moves beyond the prompt text and focuses on what to show, presenting all of the relevant information that the LLM needs to succeed. During a PoC, the team experiments with which components to include in the solution to improve performance. The following diagram shows a high-level diagram of the components that you might include when context engineering.
A modern, well-engineered context payload is a composite of several dynamically assembled components, and each serves a specific purpose. The following describes the components and provides an example for the email summarization PoC:
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System prompt or instructions – This is the foundational layer that defines the LLM's role and the core task. An example might be the following instruction to a detailed persona: "You are an expert support agent that is summarizing customer emails for internal review." To encourage a consistent format, the team can use a one-shot or few-shot prompting technique, where you provide examples of the expected output format or structure, such as an ideal summary.
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User query – The actual input query. In this example, it would be the email from the customer.
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User profile – To generate a more relevant summary, the PoC might need to add information about the customer, such as their subscription level or history.
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Memory – Including background, such as the previous conversation history, in the context helps the LLM understand the ongoing conversation.
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Tool definitions and Model Context Protocol (MCP) servers – An advanced PoC might give the model a tool to look up information, such as the customer's account details that are stored in a customer relationship management (CRM) system.
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Knowledge bases – RAG is a powerful technique for providing additional context. If the email mentions a specific product, the system can retrieve technical documentation or FAQs about that product and add them to the context. This helps you make sure that the summary is factually accurate and grounded.
Generating a prompt template requires creating a reusable structure that combines these static and dynamic components. This transforms an ad-hoc process into a repeatable and testable part of the application.
