The world of LLMs is in a constant state of flux. New models, techniques, and philosophies emerge at a breakneck pace, each promising to unlock even greater potential from these powerful tools. For a time, prompt engineering reigned supreme as the key to harnessing the power of LLMs. It was the art of crafting the perfect input to elicit the desired output. But as our ambitions with AI have grown, so too have the limitations of this approach become apparent

We’re in fact moving beyond simple, one-off interactions and toward building complex, stateful AI agents that can reason, remember, and act in the world. In this new paradigm, a new discipline is emerging as the key to success: context engineering.

The Limits of Prompting

Prompt engineering, in its essence, is about providing a clear and concise instruction to an LLM. It’s like giving a very capable, but very literal, intern a specific task. If you want a summary of a document, you provide the document and ask for a summary. If you want a piece of code, you describe the function you need. If you need copy written in the style of a marketing manager, you ask the LLM to behave (and write) like one. For a while, this was enough. We were amazed at the ability of LLMs to understand and respond to our requests.

However, as we began to build more sophisticated applications on top of LLMs, the cracks in the “prompt-only” approach began to show. Real-world tasks are rarely self-contained. They require memory, access to external knowledge, and an understanding of the broader situation. A customer service bot, for instance, needs to remember the history of its conversation with a user, access their account details, and consult a knowledge base of product information. A coding assistant needs to understand the entire codebase, not just the single file it is currently editing.

This is where prompt engineering falls short. The “prompt” is a fleeting thing, a single moment in time. It lacks the persistence and richness of information needed for complex, multi-step tasks. As Andrej Karpathy, one of the leading minds in AI, recently put it, he prefers “context engineering” over prompt engineering. He defines the former as “the delicate art and science of filling the context window with just the right information for the next step”, balancing between too little data that reduces performance, and the much information that unnecessarily raises costs.

The Rise of Context Engineering

So, what exactly is context engineering? It’s the practice of designing, building, and optimizing the entire informational ecosystem in which an LLM operates. It’s about moving beyond the single prompt and thinking about the entire context framework as a dynamic and stateful environment. Drawing on the typology suggested by Philipp Schmid, this includes:

• System Prompts: These are the foundational instructions that define the LLM’s persona, its goals, and its constraints. They are the “constitution” of the AI agent. For instance, “You are a seasoned lawyer specialized in M&A”.
• User Input: This is the immediate request from the user, the latest turn in the conversation. Eg.: “Write an effective blogpost about the latest developments of Agentic AI”.
• Conversational History: both short and long-term memory with, respectively, the current conversation and the knowledge of past interactions, allowing the LLM to maintain a coherent and stateful dialogue.
• External Knowledge (RAG): the LLMs can be given access to external data sources, such as documents, databases, or APIs. This allows it to answer questions and complete tasks that require up-to-the-minute or domain-specific information.
• Tools: Providing the LLM with access to tools, such as code interpreters, calculators, or search engines, allows it to perform actions in the world and augment its own capabilities.
• Structured Data: Presenting information in a structured format, like JSON or XML, can help the LLM to better understand and process complex data.

Context engineering, then, is not just about crafting a single, perfect prompt. It’s about architecting a system that dynamically assembles the right combination of these elements into the LLM’s context window at the right time. It’s about creating a “mental world” for the AI, a rich and dynamic environment in which it can reason, plan, and act effectively.

Context is King

The importance of context engineering cannot be overstated. In many cases, the failures of AI agents are not due to the limitations of the underlying LLM, but rather to a lack of context. An LLM can’t read your mind, and it can’t know what it doesn’t know. If you want it to perform a complex task, you need to provide it with all the necessary information and tools.

Think of it this way: if you were to ask a human expert to solve a complex problem, you wouldn’t just give them a one-sentence instruction. You would provide them with all the relevant documents, data, and background information. You would give them access to the tools they need to do their job. Context engineering is about doing the same for our AI agents.

This shift in perspective has profound implications for how we build with LLMs. It moves us away from the “black box” mentality of simply hoping for the best and toward a more systematic and architectural approach. We are no longer just “prompting” the model; we are engineering the entire context in which it operates.

Context Engineering: Next Steps

As LLMs become more powerful and more integrated into our lives, the importance of context engineering will only continue to grow. We will see the development of more sophisticated techniques for managing and optimizing the context window, such as:

• Context Compression: Techniques for summarizing and compressing large amounts of information to fit within the limited context window of the LLM.
• Memory-Augmented Architectures: frameworks for retrieving information over long periods of time, allowing AI agents to build up a lasting and reliable memory of their user interactions.
• Context-Aware Agents: The development of AI agents that can dynamically adapt their behavior based on the current context, such as the user’s emotional state, the time of day, or their physical location.

The transition from prompt engineering to context engineering represents a maturation of the field of AI. It’s a move away from the “magic” of prompting and toward a more rigorous and engineering-driven approach. A recognition that building truly intelligent systems requires more than just a clever turn of phrase, it requires a deep understanding of how to create and manage the informational environment in which these systems operate. Here at Radicalbit, we are excited to be at the forefront of this new frontier, building the tools and platforms that will enable the next generation of context-aware AI applications.

Reach out if you have any questions or want to learn more about context engineering!