Context-Engineering Challenges & Best-Practices

7 min readJul 9, 2025

Part 1: How Aggregating Conversation History into an LLM’s Context Window Can Backfire and How to Mitigate it

The practice of feeding an entire conversation history into a LLM context window, while seemingly a straightforward solution to creating a continuous and context-aware dialogue, can paradoxically degrade the model’s performance. This degradation stems from several key factors that challenge the fundamental architecture and operational efficiency of current LLMs, a phenomenon increasingly documented in academic research.

The core of the issue lies in how LLMs process information. The “context window” is the finite amount of text the model can consider at any one time when generating a response. While newer models boast increasingly large context windows, the way they “pay attention” to the information within that window is not always linear or logical from a human perspective.

Let’s explore four of the primary reasons why large, uncurated conversation histories can lead to a decline in model performance.

Context-Engineering Challenges

1. The “Lost in the Middle” Problem: Research has demonstrated that many LLMs exhibit a U-shaped performance curve when it comes to information retrieval from their context window. They tend to recall information best from the very beginning and the very end of the provided text. Information buried in the middle of a long conversation history is often overlooked or “forgotten” [1]. The authors of “Lost in the Middle: How Language Models Use Long Contexts” directly observed this phenomenon in their experiments. They state:

“We find that performance is often highest when relevant information is at the beginning or end of the context, and significantly degrades when models must access relevant information in the middle of long contexts.” [1]

This finding explains why a model might “forget” a crucial user preference mentioned mid-conversation, as the information is not positioned where the model’s attention is strongest.

2. Introduction of Noise and Distraction: Not all parts of a conversation are equally relevant to the current turn of dialogue. Small talk, corrected misunderstandings, or tangential topics from earlier in the conversation can act as “noise.” When an LLM is forced to process this irrelevant information, it can become “distracted” and lose focus on the user’s most recent query. This has been described as “Contextual Distraction Vulnerability” [2]. The paper “Breaking Focus: Contextual Distraction Curse in Large Language Models” introduces this concept, noting how easily models can be sidetracked. The authors write:

“We discover that LLMs are susceptible to contextual distraction, where irrelevant contexts in the input significantly impair their performance on downstream tasks.” [2]

This means that an overly long and unfiltered conversation history forces the model to sift through distracting, irrelevant turns of dialogue, increasing the chance of an off-topic or unfocused response.

3. Contextual Drift and Misinterpretation: As a conversation evolves, the context can shift. An LLM processing the entire history may latch onto an outdated context, leading to misinterpretations. This is exacerbated in scenarios like Retrieval-Augmented Generation (RAG), where simply adding more documents or conversational turns to the context doesn’t guarantee better performance. In fact, it can introduce “hard negatives” — passages that are topically related but incorrect — which confuse the model [4]. The paper “Long-Context LLMs Meet RAG” highlights this challenge, explaining that more context is not always better. They explain:

“…merely extending the context length for RAG does not guarantee performance improvements, and in some cases, it may even degrade performance. This is because longer contexts can introduce more noise and hard-to-distinguish ‘negative’ documents, making it difficult for the LLM to focus on the ‘positive’ documents that contain the answer.” [4]

This directly illustrates how outdated or slightly incorrect information from earlier in a long conversation can be misinterpreted by the model as relevant, leading to flawed reasoning and incorrect answers.

4. Increased Computational Cost and Latency: While not a direct degradation of the quality of the output, the computational overhead of processing a long context window is significant. The attention mechanism in standard Transformer architectures has a computational complexity that scales quadratically with the length of the input sequence. This results in prohibitive resource requirements and slower response times for long-form dialogue [5]. The paper “InftyThink: Breaking the Length Limits of Long-Context Reasoning” points to this as a primary bottleneck for handling long contexts. The authors note:

“Standard attention mechanisms in LLMs exhibit quadratic computational scaling with sequence length, making long-context processing resource-intensive and slow.” [5]

This inefficiency leads to a frustrating user experience due to delays and may force developers to use smaller, less capable models to manage costs, indirectly degrading response quality.

In essence, while providing conversation history is crucial for coherent dialogue, simply “stuffing” the entire raw history into the context window is a crude and often counterproductive approach.

Sophisticated techniques for Context-Engineering

More sophisticated techniques are being developed to provide LLMs with the necessary context in a more efficient and performance-friendly manner.

In this section I’ll explore some of the most prominent and sophisticated techniques being used today. Using these more sophisticated methods, helps developers create AI agents that are more efficient, cost-effective and more intelligent, reliable, and capable of maintaining a truly personal and continuous user experience.

1. Conversational Summarization

This is the most intuitive approach. Instead of providing the full, verbatim history, a separate process summarizes the conversation as it progresses.

How it works: After a few turns of conversation, another LLM call is made in the background to create a concise summary of the exchange so far. This summary is then either used on its own or prepended to the most recent few turns of the conversation in the context window. For very long conversations, this can be a recursive process where the existing summary is updated with a summary of the newest messages.
Why it helps: It drastically reduces the number of tokens (words) that need to be processed, cutting down on computational cost and latency. It also acts as a form of “noise cancellation,” filtering out small talk and focusing on the core points of the dialogue, which helps mitigate the “lost in the middle” problem.

2. Retrieval-Augmented Generation (RAG)

This is arguably the most powerful and widely adopted technique for providing long-term memory and external knowledge to LLMs. It treats the conversation history not as a single text block, but as a searchable database.

How it works:

Indexing: As the conversation happens, each turn (or a chunk of turns) is converted into a numerical representation called a “vector embedding.” These embeddings capture the semantic meaning of the text. They are then stored in a specialized database called a vector database.
Retrieval: When the user sends a new message, that new message is not immediately combined with the entire history. Instead, it is also converted into a vector embedding.
Search: The system uses this new embedding to perform a similarity search in the vector database, instantly finding the most relevant past messages (e.g., a user mentioning a specific preference, a question that was asked 30 minutes ago).
Augmentation: These highly relevant, retrieved snippets of conversation are then inserted into the LLM’s context window along with the user’s latest message.

Why it helps: This is extremely efficient. The LLM’s context window remains small and focused only on the most pertinent information, directly avoiding the cost, latency, and noise issues of a full context. It’s like having a perfect librarian who finds the exact paragraphs you need from a massive library instead of making you read every book.

3. Entity and Preference Extraction

This method involves treating user preferences and key data points as structured data to be explicitly tracked.

How it works: An LLM or another natural language processing model scans the conversation specifically to identify and extract key pieces of information, known as “entities.” These could be names, order numbers, user preferences, stated goals, etc. This information is then stored in a structured format (like a JSON object or a simple database table) that represents the user’s profile.
Example: {“user_id”: “123”, “preferences”: {“shirt_size”: “Large”, “color”: “blue”}, “last_order_id”: “XYZ-987”}
Why it helps: This provides a highly reliable and concise memory of critical user details. Instead of hoping the LLM spots a preference in a long conversation, that preference is explicitly passed into the context window as a clear instruction (e.g., “Remember, the user’s shirt size is Large”). This prevents contextual drift and misinterpretation of core facts.

4. Hybrid Approaches and Memory Architectures

The most advanced systems rarely rely on a single technique. They combine them into a sophisticated “memory module” for the agent.

How it works: A system might use RAG to recall specific factual memories from a long conversation, employ a summarization module to keep track of the recent conversational flow, and use entity extraction to maintain a persistent user profile. The memory module then intelligently decides which combination of these context sources is most relevant for the current query and assembles a perfectly tailored context window for the LLM on the fly.

Context-Engineering Techniques: A Comparison

References

[1] Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2023). Lost in the Middle: How Language Models Use Long Contexts. arXiv preprint arXiv:2307.03172.

[2] Li, X., Liu, C., Yang, Y., Zhang, Y., & Dong, Y. (2025). Breaking Focus: Contextual Distraction Curse in Large Language Models. arXiv preprint arXiv:2502.01609.

[3] Zhang, C., Liu, Z., Niu, B., & Zhang, Y. (2025). Focus Directions Make Your Language Models Pay More Attention to Relevant Contexts. arXiv preprint arXiv:2503.23306.

[4] Shah, S., Gupta, N., Soni, S., Feizi, S., & Arora, C. (2024). Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG. arXiv preprint arXiv:2410.05983.

[5] Zhou, Y., Hu, W., Wang, C., & Zhang, Q. (2025). InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models. arXiv preprint arXiv:2503.06692.