Large Language Models like GPT-4 and Claude have taken the AI world by storm, but their true power isn’t just in raw model weights – it lies in how we shape their context. By carefully designing prompts, incorporating memory, grounding models in external data, and controlling what information is retrieved, practitioners can turn a clever LLM into a reliable, task-solving system. This emerging discipline of context engineering is being hailed as a new programming paradigm for LLMs, elevating prompt engineering into a broader, more systematic practice. In this blog post, we’ll explore why context engineering matters, what’s driving its rise, and how data scientists can apply it in practice to build more effective AI applications. (P.S. At ODSC AI West this October 28-30, experts will unpack these ideas in depth – more on that later.)

What is Context Engineering?

Context engineering involves designing an LLM’s input, environment, and retrieval to guide its outputs. It’s about building dynamic systems that provide the right information and tools in the correct format for a task, going beyond just clever prompt wording. While prompt engineering focuses on the immediate query, context engineering considers the model’s entire “working memory,” including system instructions, user prompts, conversation history, retrieved facts, and tool outputs.

For instance, an LLM’s context might include developer instructions (defining its role), the user’s current question, data from a knowledge base, conversation summaries, and required output formats. By consciously structuring these elements, we can achieve more accurate and consistent results.

Prompt engineering is a subset of context engineering. Even a well-written prompt needs critical information and guidance to succeed. Context engineering takes a system-level view, treating the LLM as part of a pipeline that dynamically gathers and formats context for each interaction, rather than a black box. This approach acknowledges that LLMs need proper setup to succeed; failures often stem from a lack of context, instructions, or tools, not the model’s intelligence.

In essence, context engineering “programs” the AI by controlling its context, not by adjusting its internal weights.

Why Context Engineering Is Hot Right Now

Exploding Context Windows

Not long ago, mainstream LLMs could handle only a few thousand tokens of context (roughly a few pages of text). Today, that limit is being shattered – some new models boast context windows in the hundreds of thousands or even millions of tokens. (Meta’s latest Llama 4 model, for example, supports a staggering 10 million tokens in its context window.) This dramatic expansion unlocks new possibilities: imagine feeding entire books or extensive logs into a model in one go. However, it also raises the stakes for context design. 

Just because you can stuff in more information doesn’t mean the model will use it effectively. In fact, dumping too much irrelevant text can confuse the model or dilute its attention – studies show that all else equal, extremely long prompts tend to decrease accuracy if they include a lot of extraneous information. The upshot is that bigger context windows are empowering, but to capitalize on them, one must carefully engineer what goes into that window. Choosing the right 100K out of a million tokens is the new art!

Rise of Agentic AI

Another factor fueling interest in context engineering is the rise of “agentic” AI systems – essentially, AI agents that conduct multi-step reasoning, tool use, and long-running dialogues autonomously. Applications like AutoGPT, LangChain agents, and other AI “agents” have exploded in popularity. These agents rely heavily on well-managed context. Unlike a single-turn Q&A, an agent might carry on a conversation or task for dozens or even hundreds of steps, accumulating information and intermediate results along the way. Maintaining a coherent context throughout is vital for such agents to make good decisions. In fact, some experts argue that crafting and managing context is effectively the number one job when building AI agents. 

Without robust context handling, agents can quickly go off track or forget important details from earlier steps. Large context windows help, but they aren’t a silver bullet – developers must use strategies like summarizing old interactions, selectively retrieving relevant facts, and keeping the agent’s “working notes” (scratchpads) to ensure the agent remembers what it should. As Anthropic researchers noted, agents often engage in conversations spanning hundreds of turns, which “require careful context management strategies” to work reliably. 

The surging interest in agent-based AI has therefore shone a spotlight on context engineering as a critical discipline.

Enterprise Demands: Reliability and Trust

In enterprise AI applications, the need for reliability, trustworthiness, and reproducibility of model outputs is paramount – and these needs are driving context-centric techniques. Businesses want AI systems that don’t hallucinate facts, that stay consistent with company policy, and whose answers can be traced back to credible sources. Achieving this with an off-the-shelf LLM means grounding the model in the organization’s own knowledge and rules via context. This is a significant reason why Retrieval-Augmented Generation has gained popularity: RAG combines an LLM with a company’s private data store, pulling in relevant documents as context, so the model’s answers are based on real enterprise knowledge rather than just its public training data. By injecting domain-specific context on the fly, we get outputs that align with an organization’s unique expertise and up-to-date information, greatly reducing errors and hallucinations.

Context engineering also supports reproducibility – if you always fetch the same facts for a given query, the model’s responses become more consistent. And for compliance and safety, engineers can insert policy guidelines or content filters into the prompt context (for example, a system message that enumerates forbidden categories of responses). In short, as enterprises roll out LLMs, they are finding that success hinges on meticulous context design to make these models trustworthy and auditable in real-world settings.

Tooling Ecosystem & Democratization

Finally, the boom in developer tooling around LLMs has made context engineering far more accessible – fueling its rise as a practice. In the past year, we’ve seen an explosion of frameworks and libraries like LangChain, LlamaIndex, and others dedicated to prompt orchestration, memory management, and retrieval pipelines. Need to build an LLM-powered chatbot that pulls answers from a vector database of documents? Tools like LangChain enable you to embed your documents with just a few lines of code, search them on the fly, and insert the results into the prompt. Likewise, we now have readily available vector databases (Pinecone, Chroma, Weaviate, etc.) and orchestration frameworks that handle the heavy lifting of context management (tracking conversation state, calling external APIs, and feeding results back into the LLM, etc.). 

This rich ecosystem means you don’t have to reinvent the wheel to implement context engineering best practices – it’s increasingly plug-and-play. The result is a democratization of context manipulation: more practitioners, not just research labs, can experiment with advanced prompting strategies, memory augmentation, and tool-use workflows. In turn, this has accelerated innovation and knowledge-sharing around context engineering, making it one of the hottest areas in applied AI right now.

Practical Applications of Context Engineering

Retrieval-Augmented Generation (RAG)

One of the most widespread practical uses of context engineering is Retrieval-Augmented Generation, where an LLM is paired with a knowledge retrieval system. In RAG, the model is not left to generate answers from its parametric memory alone – instead, it’s provided with relevant snippets of external text (retrieved from, say, an enterprise document database or the web) as additional context before it formulates a response. This approach has proven incredibly useful for enterprise AI applications. By grounding the LLM in up-to-date, authoritative information, RAG ensures the AI’s output stays factual and relevant to the user’s query. 

For example, a customer support assistant built with RAG might pull the latest product manual or a specific knowledge base article related to a user’s question, and include that in the prompt. The LLM then bases its answer on that retrieved text, greatly reducing hallucinations and making its responses traceable. In essence, RAG exemplifies context engineering in action: it’s all about feeding the model the right data at query time.

Personalization and User-Specific Context

Context engineering also enables personalization in AI interactions. Rather than giving every user the same generic response, an LLM system can tailor its outputs by incorporating user-specific context. This might include a user’s past preferences, profile information, or interaction history. For instance, a personal AI assistant could remember a user’s dietary restrictions and travel plans from previous chats, and use that context to personalize recommendations or answers in the future. Techniques like long-term memory storage and retrieval are key here – the system might save a summary of each user session or key facts about the user, and later fetch those details when relevant. 

This way, the model’s responses feel more context-aware and individualized. A simple example: if a user told the chatbot last week, “I don’t eat seafood,” a context-engineered system can store that fact and retrieve it next time the user asks for restaurant suggestions, ensuring the recommendations are compatible. In short, by designing prompts to include dynamic user data, we can move from one-size-fits-all outputs to more personalized, engaging interactions. (In fact, researchers have demonstrated LLM agents that fetch a user’s stated preferences from previous conversations as part of their context.

Multi-Turn Conversations and Memory

Anyone who has used ChatGPT or similar tools knows that maintaining context across a multi-turn conversation is crucial. Context engineering techniques make this possible by giving the model a form of memory. In practice, this often means that as a dialogue with a user progresses, the system must distill earlier exchanges into a condensed form and include that in subsequent prompts. For example, after a long discussion, an LLM might not fit the entire history in its context window – but a context-engineered solution can generate a running summary or extract key facts and feed those forward. This allows the model to “remember” what was said before. 

Another approach is to tag important pieces of information (e.g., “User’s goal: automate data pipeline”) and always carry those tags in the prompt for context. Building systems that remember beyond a single session is an active area of development as well. Some applications create long-term memory vectors or knowledge graphs for each user or conversation, so that even if you come back days later, the AI can retrieve your past context to inform new interactions. 

The bottom line is that by engineering how conversation state is preserved and reintroduced, we enable LLMs to engage in coherent, lengthy dialogues rather than just one-off Q&A. This significantly improves user experience and is a direct application of context engineering principles (for instance, summarizing a conversation and using that summary in future prompts is a known good practice.

Guardrails and Controlled Generation

Context engineering implements guardrails to guide LLM behavior for safety, consistency, or compliance. System prompts, a hidden part of the context, instruct models on what to avoid, like violating content policies. Injecting rules, style guides, or test criteria into the context fences in the model’s behavior without altering it, useful for enterprise deployments with off-limits topics or specific tones.

Context can also be used for on-the-fly evaluation, prompting the model to double-check or justify answers with additional context like correct answers or step-by-step reasoning templates. Anthropic’s “Constitutional AI” uses principles to guide model outputs, effectively feeding rules in context for critique and refinement. Robust AI applications combine multiple layers of context, including factual grounding, safety instructions, and formatting requirements. These context engineering elements act as guardrails, transforming generative models into governed, reliable assistants.

Skills and Mindset for Data Scientists

Context engineering is vital for LLM success, shifting from a “model-first” to a “pipeline-first” mindset. It involves orchestrating data flow and designing interacting system components like prompts, retrieval, and memory. A key skill is knowing when to engineer context versus fine-tune the model, often favoring context tweaks for faster, cost-effective adaptation. While fine-tuning has its place, retrieval from external data sources is the “canonical approach” for new knowledge. This approach integrates traditional ML practices with new techniques, treating prompts and context as testable, versionable data pipelines. Experimentation frameworks are crucial for optimizing the context pipeline. Mastering context engineering requires diverse skills, including prompt crafting, NLP/IR knowledge, and iterative testing, offering a significant opportunity for data professionals.

Where Context Engineering is Headed

As LLM applications grow, “ContextOps” will emerge to manage context, much like MLOps manages models. This includes versioning prompts, monitoring knowledge sources, and automating context updates. Future platforms may offer dynamic prompt assembly, knowledge graph integration, and compliance checks. Context engineering will become a repeatable, governable process, acting as the “program” for LLMs and requiring DevOps-like tooling.

LLMs will integrate more deeply with enterprise knowledge graphs and databases. While current RAG often uses unstructured text, future context pipelines will combine structured data (SQL, graph databases) with unstructured retrieval. This blends broad unstructured knowledge with precise structured data, crucial for companies leveraging all their information assets.

Context engineering will also be central to AI agent workflows, requiring standardized ways for agents to store, share, and recover from context-related errors. This indicates context engineering is a foundational and evolving aspect of AI development. Data scientists and ML engineers specializing in this area will become key experts for future AI solutions, as it’s becoming one of the most important skills for AI practitioners.

Context Engineering at ODSC AI West

Eager to learn about context engineering in AI? ODSC AI West 2025 is the place to be. The conference will heavily feature context engineering, including a keynote by Harrison Chase of LangChain titled “Context Engineering for AI Agents.” Topics like RAG, agentic AI, and Applied LLMOps will be covered in talks and workshops, with dedicated tracks for GenAI X Summit and Agentic AI. This event offers practical skills for data scientists and ML engineers, providing actionable insights into AI development, best practices from pioneers, and networking opportunities. ODSC AI West is crucial for staying ahead in the evolving field of applied LLMs and context engineering.

Final Thoughts: Don’t Just Use LLMs—Engineer Them

Context engineering isn’t just another tool in the AI toolbox—it’s the missing link between what large language models can do and what they should do in real-world applications. It’s how we shift from generic outputs to tailored, trusted AI systems that solve real problems.

Think of it this way: model architecture may get the headlines, but it’s context that delivers results. And for data scientists, ML engineers, and AI practitioners, that makes context engineering one of the most strategic skills to build right now—where technical precision meets creative input design.

Master it, and you’re not just building smarter systems—you’re shaping how AI serves people, scales responsibly, and earns trust.

Ready to Lead the Next AI Wave?

If you’re serious about getting ahead of this shift—not watching it from the sidelines—join us at ODSC AI West. You’ll:

• Learn directly from pioneers driving real-world context engineering
• Get hands-on with techniques that increase model accuracy, alignment, and efficiency
• Build your network with top minds pushing the boundaries of LLMs

🔥 Context engineering is heating up. Be where the action is—and position yourself at the forefront of the AI movement that’s defining the future.

👉 Register now and reserve your seat at ODSC AI West