Image by Author | Canva# Introduction
There is no doubt that large language models can do amazing things. But apart from their internal knowledge base, they heavily depend on the information (the context) you feed them. Context engineering is all about carefully designing that information so the model can succeed. This idea gained popularity when engineers realized that simply writing clever prompts is not enough for complex applications. If the model doesn’t know a fact that’s needed, it can’t guess it. So, we need to assemble every piece of relevant information so the model can truly understand the task at hand.
Part of the reason the term ‘context engineering’ gained attention was due to a widely shared tweet by Andrej Karpathy, who said:
+1 for ‘context engineering’ over ‘prompt engineering’. People associate prompts with short task descriptions you would give an LLM in your day-to-day use, whereas in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step…
This article is going to be a bit theoretical, and I will try to keep things as simple and crisp as I can.
# What Is Context Engineering?
If I received a request that said, ‘Hey Kanwal, can you write an article about how LLMs work?’, that’s an instruction. I would write what I find suitable and would probably aim it at an audience with a medium level of expertise. Now, if my audience were beginners, they would hardly understand what’s happening. If they were experts, they might consider it too basic or out of context. I also need a set of instructions like audience expertise, article length, theoretical or practical focus, and writing style to write a piece that resonates with them.
Likewise, context engineering means giving the LLM everything from user preferences and example prompts to retrieved facts and tool outputs, so it fully understands the goal.
Here’s a visual that I created of the things that might go into the LLM’s context:
Context engineering includes instructions, user profile, history, tools, retrieved docs, and more | Image by AuthorEach of these elements can be viewed as part of the context window of the model. Context engineering is the practice of deciding which of these to include, in what form, and in what order.
# How Is Context Engineering Different From Prompt Engineering?
I will not make this unnecessarily long. I hope you have grasped the idea so far. But for those who didn’t, let me put it briefly. Prompt engineering traditionally focuses on writing a single, self-contained prompt (the immediate question or instruction) to get a good answer. In contrast, context engineering is about the entire input environment around the LLM. If prompt engineering is ‘what do I ask the model?’, then context engineering is ‘what do I show the model, and how do I manage that content so it can do the task?’
# How Context Engineering Works
Context engineering works through a pipeline of three tightly connected components, each designed to help the model make better decisions by seeing the right information at the right time. Let’s take a look at the role of each of these:
// 1. Context Retrieval and Generation
In this step, all the relevant information is pulled in or generated to help the model understand the task better. This can include past messages, user instructions, external documents, API results, or even structured data. You might retrieve a company policy document for answering an HR query or generate a well-structured prompt using the CLEAR framework (Concise, Logical, Explicit, Adaptable, Reflective) for more effective reasoning.
// 2. Context Processing
This is where all the raw information is optimized for the model. This step includes long-context techniques like position interpolation or memory-efficient attention (e.g., grouped-query attention and models like Mamba), which help models handle ultra-long inputs. It also includes self-refinement, where the model is prompted to reflect and improve its own output iteratively. Some recent frameworks even allow models to generate their own feedback, judge their performance, and evolve autonomously by teaching themselves with examples they create and filter.
// 3. Context Management
This component handles how information is stored, updated, and used across interactions. This is especially important in applications like customer support or agents that operate over time. Techniques like long-term memory modules, memory compression, rolling buffer caches, and modular retrieval systems make it possible to maintain context across multiple sessions without overwhelming the model. It is not just about what context you put in but also about how you keep it efficient, relevant, and up-to-date.
# Challenges and Mitigations in Context Engineering
Designing the perfect context isn’t just about adding more data, but about balance, structure, and constraints. Let’s look at some of the key challenges you might encounter and their potential solutions:
- Irrelevant or Noisy Context (Context Distraction): Feeding the model too much irrelevant information can confuse it. Use priority-based context assembly, relevance scoring, and retrieval filters to pull only the most useful chunks.
- Latency and Resource Costs: Long, complex contexts increase compute time and memory use. Truncate irrelevant history or offload computation to retrieval systems or lightweight modules.
- Tool and Knowledge Integration (Context Clash): When merging tool outputs or external data, conflicts can occur. Add schema instructions or meta-tags (like
@tool_output) to avoid format issues. For source clashes, try attribution or let the model express uncertainty. - Maintaining Coherence Over Multiple Turns: In multi-turn conversations, models may hallucinate or lose track of facts. Track key information and selectively reintroduce it when needed.
Two other important issues: context poisoning and context confusion have been well explained by Drew Breunig, and I encourage you to check that out.
# Wrapping Up
Context engineering is no longer an optional skill. It is the backbone of how we make language models not just respond, but understand. In many ways, it is invisible to the end user, but it defines how useful and intelligent the output feels. This was meant to be a gentle introduction to what it is and how it works.
If you are interested in exploring further, here are two solid resources to go deeper:
### Items for Human Review:
* **Andrej Karpathy Tweet**: The article quotes a “widely shared tweet by Andrej Karpathy.” For credibility and reader convenience, it would be best to find the original tweet and link to it directly. The quoted text should also be checked against the original for accuracy.
* **External Links**: The article links to an article by Drew Breunig, an arXiv paper, and a deepwiki page. A human editor should verify these links are active, reputable, and point to the intended content before publication. The arXiv paper ID (2507.13334) appears to be a placeholder for a future publication and will need to be confirmed.
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
