LLM conversation quality degrades as the context window fills. This is not a subjective impression. Research by Liu et al. (2023) found that models perform significantly worse on information placed in the middle of long contexts compared to information at the beginning or end, an effect called "lost in the middle." For production applications with long conversations or sessions, context management is a first-class engineering concern.
The five strategies covered here are: sliding window memory, hierarchical summarization, RAG for conversation memory, explicit context tracking, and stateless design. Most applications need one or two of these, not all five.
Why Context Degrades
The "lost in the middle" problem (Liu et al., "Lost in the Middle: How Language Models Use Long Contexts," 2023) documents a consistent pattern: when relevant information is placed at the beginning or end of a long context, models retrieve it accurately. When the same information is buried in the middle of a long context, retrieval accuracy drops substantially.
The practical consequence for applications: as conversations grow, your system prompt (placed at the start) is progressively pushed toward the middle of the context. Instructions given early in the conversation become less reliably followed. Context established early is under-weighted.
A second effect: as context grows, the attention the model pays to any individual token is distributed across more tokens. Early messages receive progressively less "attention" in the mathematical sense of the transformer mechanism. This is why long conversations often feel like the model has forgotten what was established at the beginning.
Strategy 1: Sliding Window
The simplest approach. Keep only the most recent N messages in the context, discard older ones.
Implementation: set a maximum message count (e.g., 10 exchanges). When adding a new message would exceed the limit, drop the oldest message pair (user message + assistant response).
Appropriate for: conversational applications where recent context matters more than history, customer support chatbots, simple Q&A tools.
Limitation: hard cutoffs lose information abruptly. A user might reference something from message 3 in message 15, and the model will have no memory of it.
MAX_MESSAGES = 10 # keep last 10 exchanges
def trim_messages(messages: list, max_messages: int = MAX_MESSAGES) -> list:
# Always keep the system message if present
system = [m for m in messages if m["role"] == "system"]
non_system = [m for m in messages if m["role"] != "system"]
if len(non_system) > max_messages * 2:
non_system = non_system[-(max_messages * 2):]
return system + non_system
