October 30, 2025
Introduction to Context Engineering
As we have explored the intricacies of prompt engineering, it has become clear that the quality of an AI model’s output is heavily dependent on the quality of its input. However, the prompt itself is only one piece of the puzzle. To build truly intelligent and capable AI systems, we must also consider the broader context in which the model operates. This is where context engineering comes into play. It is the practice of designing systems that strategically manage the information provided to an AI model, ensuring that it has the right context, at the right time, to perform its task effectively.
Context engineering represents a significant evolution in our approach to AI system design. While prompt engineering focuses on crafting the perfect instruction, context engineering is concerned with curating the perfect environment for the AI to operate in. This includes managing the information that is fed into the model’s context window, integrating external knowledge sources, and even providing the model with a form of long-term memory. This article, the third in our series, will provide a comprehensive overview of context engineering, exploring its core concepts, key techniques, and practical applications.
How It Differs from Prompt Engineering
It is important to distinguish between prompt engineering and context engineering, as they represent two distinct but complementary disciplines. Prompt engineering is primarily focused on the art of crafting the prompt itself – the specific instruction or query that is given to the AI model. Context engineering, on the other hand, is concerned with the broader set of information that is provided to the model alongside the prompt. This can include anything from the conversation history and user profile to external documents and real-time data.
As the team at LlamaIndex aptly puts it, “While the term ‘prompt engineering’ focused on the art of providing the right instructions to an LLM at the forefront… ‘context engineering’ puts a lot more focus on filling the context window of an LLM with the most relevant information, wherever that information may come from.” [6]
In essence, prompt engineering is about asking the right question, while context engineering is about providing the right information to answer that question. Both are essential for building effective AI systems, but they address different aspects of the problem.
Why Context Engineering Matters for Modern AI Systems
The importance of context engineering has grown in tandem with the increasing sophistication of AI models. As LLMs become more powerful and capable, they are being applied to a wider range of tasks that require a deep understanding of the world and the ability to reason about complex information. Context engineering is crucial for enabling these advanced capabilities, as it provides the model with the necessary information to perform these tasks effectively.
Furthermore, as we move towards more agentic AI systems that can act autonomously and interact with the world, the need for effective context management becomes even more critical. These agents need to be able to perceive their environment, remember past interactions, and access external knowledge in order to make intelligent decisions. Context engineering provides the framework for building these capabilities, paving the way for a new generation of intelligent and autonomous AI systems.
Understanding the Context Window
At the heart of context engineering lies the concept of the context window. The context window is the finite amount of information that an AI model can “see” at any given moment. It is the model’s short-term memory, and it encompasses everything that is provided as input, including the system prompt, the user’s query, the conversation history, and any additional context that is injected into the prompt. The size of the context window is measured in tokens, which can be thought of as words or parts of words.
Token Limits and Constraints
Every large language model has a maximum context window size, which can range from a few thousand tokens to over a million in the most advanced models. This finite limit presents a significant challenge for context engineering. As the conversation with an AI model progresses, the context window can quickly fill up with the conversation history, leaving less room for new information. This can lead to a phenomenon known as “context drift,” where the model starts to lose track of the earlier parts of the conversation and its responses become less coherent.
The challenge, as highlighted by many practitioners, is not just about fitting information into the context window, but about ensuring that the right information is present at the right time. This is the central problem that context engineering seeks to solve.
The Challenge of Context Management
Effective context management is a delicate balancing act. On the one hand, we want to provide the model with as much relevant information as possible to ensure that it has the context it needs to perform its task effectively. On the other hand, we need to be mindful of the token limit and avoid overwhelming the model with unnecessary or redundant information. This requires a strategic approach to context selection, where we prioritize the most important information and discard anything that is not directly relevant to the current task.
Furthermore, the way in which information is structured and presented within the context window can have a significant impact on the model’s performance. A well-organized context, with clear headings and a logical flow, can help the model to better understand the information and generate more accurate and coherent responses. This is where techniques like structured data injection and prompt formatting come into play, which we will explore in more detail in the following sections.
Context Sources and Integration
To effectively manage the context window, it is essential to understand the various sources of context that can be used to inform an AI model. These sources can be broadly categorized into two groups: internal context, which is generated within the AI system itself, and external context, which is drawn from outside sources. By strategically combining these different types of context, we can create a rich and dynamic environment for the AI model to operate in.
Internal Context
Internal context is the information that is generated and managed within the AI system itself. This includes:
- User Prompts and Instructions: The most immediate source of context is the user’s own input. This includes the specific question or instruction that the user provides, as well as any additional information or constraints that they specify.
- Conversation History: In a conversational AI system, the history of the conversation is a crucial source of context. It provides the model with a record of past interactions, allowing it to maintain a coherent and consistent dialogue.
- User Profile: Information about the user, such as their name, preferences, and past interactions, can be used to personalize the AI’s responses and tailor them to the user’s specific needs.
External Context
External context is the information that is drawn from outside the AI system. This can include a wide range of sources, such as:
- Retrieved Information from Knowledge Bases: One of the most powerful ways to enhance an AI model’s capabilities is to provide it with access to a knowledge base of relevant information. This can be a collection of documents, a database of structured data, or a real-time data feed.
- Tool Descriptions and Resources: As we saw in the previous article, AI models can be given access to a wide range of external tools and APIs. The descriptions of these tools, as well as the information they provide, can be a valuable source of context.
- Real-Time Data: For tasks that require up-to-date information, such as news summarization or financial analysis, real-time data feeds can be integrated into the context window to provide the model with the latest information.
By combining these different sources of context, we can create a rich and dynamic information environment that enables the AI model to perform a wide range of tasks with a high degree of accuracy and relevance.
Multi-Source Knowledge Integration
In many real-world applications, a single source of knowledge is not enough. To tackle complex problems and provide comprehensive answers, AI systems often need to draw upon information from multiple, diverse sources. Multi-source knowledge integration is the practice of combining information from a variety of knowledge bases, databases, and real-time data feeds to create a unified and holistic view of the world. This is a key aspect of advanced context engineering, as it enables the AI model to access and reason about a much broader range of information than would be possible with a single knowledge source.
Working with Multiple Knowledge Bases
Integrating multiple knowledge bases presents a number of challenges. Each knowledge base may have its own unique structure, format, and access methods. To effectively combine information from these different sources, we need to develop a strategy for:
- Data Federation: Creating a unified view of the data from multiple sources, without having to physically move or copy the data.
- Data Harmonization: Reconciling differences in data formats, schemas, and semantics to ensure that the information from different sources is consistent and comparable.
- Query Routing: Determining which knowledge base is most likely to contain the answer to a given question and routing the query accordingly.
Tool Integration Strategies
In addition to knowledge bases, AI systems can also be given access to a wide range of external tools and APIs. These tools can be used to perform a variety of tasks, such as performing calculations, retrieving real-time data, or interacting with other software systems. The integration of these tools into the context window is a key aspect of multi-source knowledge integration, as it allows the AI model to not only access information but also to act upon it.
Effective tool integration requires a clear and concise description of each tool, including its purpose, parameters, and expected output. This information is then provided to the AI model as part of its context, allowing it to understand when and how to use each tool.
Long-Term Memory Management
While the context window provides the AI model with a form of short-term memory, it is not sufficient for tasks that require the model to remember information over a long period of time. To address this limitation, we can implement a form of long-term memory, which allows the model to store and retrieve information from a persistent knowledge store. This is a crucial aspect of building more advanced AI systems, as it enables them to learn from past interactions and maintain a consistent and coherent identity over time.
There are several different approaches to implementing long-term memory, each with its own advantages and disadvantages. The choice of which approach to use will depend on the specific requirements of the task at hand.
Vector Memory Blocks
Vector memory blocks are a popular approach to long-term memory that involves storing information in a vector database. A vector database is a specialized type of database that is designed to store and retrieve high-dimensional vectors, such as the embeddings generated by a large language model. When new information is added to the memory, it is first converted into a vector embedding and then stored in the vector database. To retrieve information from the memory, a query is also converted into a vector embedding, and the database then returns the most similar vectors from its store.
This approach is particularly well-suited for tasks that require the model to retrieve information based on semantic similarity. For example, a chatbot could use a vector memory block to remember past conversations with a user and retrieve relevant information based on the topic of the current conversation.
Fact Extraction Memory Blocks
Fact extraction memory blocks are another approach to long-term memory that involves extracting structured facts from the conversation history and storing them in a structured database. This approach is particularly well-suited for tasks that require the model to remember specific pieces of information, such as the user’s name, preferences, or past orders.
By storing information in a structured format, we can ensure that it is accurate and consistent, and we can easily query it to retrieve specific pieces of information. This is in contrast to vector memory blocks, which are better suited for retrieving information based on semantic similarity.
Static Memory Blocks
Static memory blocks are the simplest form of long-term memory, and they involve storing a static piece of information that is always available to the model. This can be a set of instructions, a system prompt, or a piece of background information that is relevant to the task at hand. While simple, static memory blocks can be a powerful way to provide the model with a consistent and reliable source of context.
By combining these different types of memory, we can create a sophisticated long-term memory system that enables the AI model to learn from past interactions and maintain a consistent and coherent identity over time.
Practical Examples
To illustrate the power of context engineering, let’s explore some practical examples of how it can be used to build more intelligent and capable AI systems.
Building a Context-Aware Chatbot
A common application of context engineering is in the development of context-aware chatbots. These chatbots are able to maintain a coherent and consistent conversation with a user by remembering past interactions and using that information to inform their responses. This is typically achieved by using a combination of conversation history and long-term memory.
The Architecture:
- Conversation History: The chatbot maintains a record of the current conversation, which is included in the context window for each turn of the conversation.
- Long-Term Memory: The chatbot also has access to a long-term memory store, which can be a vector database or a structured database. This memory store is used to remember information from past conversations, such as the user’s name, preferences, and past orders.
- Context Injection: Before generating a response, the chatbot first retrieves relevant information from its long-term memory and injects it into the context window. This provides the model with the necessary context to generate a personalized and relevant response.
Multi-Source Information Retrieval
Another powerful application of context engineering is in the development of multi-source information retrieval systems. These systems are able to answer complex questions by drawing upon information from multiple, diverse sources, such as a company’s internal knowledge base, a public-facing website, and a real-time news feed.
The Architecture:
- Knowledge Base Integration: The system is connected to multiple knowledge bases, each with its own unique structure and format.
- Query Routing: When a user asks a question, the system first analyzes the question to determine which knowledge base is most likely to contain the answer. It then routes the query to the appropriate knowledge base.
- Information Synthesis: The system then retrieves the relevant information from the knowledge base and synthesizes it into a single, coherent answer.
Memory-Enhanced Applications
Context engineering can also be used to build memory-enhanced applications that are able to learn from past interactions and adapt their behavior over time. This is particularly useful for applications that require a high degree of personalization, such as a recommendation engine or a personalized learning platform.
The Architecture:
- User Profile: The application maintains a detailed profile of each user, including their preferences, past interactions, and learning history.
- Behavioral Tracking: The application tracks the user’s behavior and uses that information to update their profile in real-time.
- Personalized Recommendations: The application then uses the user’s profile to generate personalized recommendations, such as suggesting new products, articles, or learning materials that are likely to be of interest to the user.
These examples illustrate the wide range of applications for context engineering and demonstrate how it can be used to build more intelligent, capable, and personalized AI systems.
Conclusion and Best Practices
Context engineering represents a fundamental shift in how we approach the design and development of AI systems. By moving beyond the narrow focus on prompt crafting and embracing a holistic view of context management, we can build AI systems that are more intelligent, capable, and reliable. The techniques we have explored in this article, from multi-source knowledge integration to long-term memory management, provide a powerful toolkit for building the next generation of AI applications.
As you begin to implement context engineering in your own work, remember that it is an iterative and experimental process. Start with a clear understanding of your task and the information that is required to complete it. Then, carefully design your context management strategy, taking into account the limitations of the context window and the trade-offs between accuracy and efficiency. By following these best practices and continuously refining your approach, you can build AI systems that deliver exceptional results and provide real-world value.
Best Practices for Context Engineering
- Start with a clear understanding of the task and the information required to complete it.
- Prioritize the most important information and discard anything that is not directly relevant.
- Use structured data and clear formatting to help the model understand the context.
- Implement a robust long-term memory system to enable the model to learn from past interactions.
- Continuously monitor and refine your context management strategy based on performance metrics.
By adhering to these best practices and leveraging the techniques we have explored in this article, you will be well-equipped to build sophisticated AI systems that can reason about and interact with the world in a more meaningful way.
References
[3] IBM – Prompt Engineering Techniques
[4] Generated Knowledge Prompting for Commonsense Reasoning
[5] Tree of Thoughts: Deliberate Problem Solving with Large Language Models