What are LLM Context and Memory, and Why is Efficient Usage Important?

One-Line Definition

LLM context and memory are core mechanisms that determine the amount and scope of information an AI can process and remember at once during a conversation.

Why LLM Context and Memory Now?

Early LLMs were only capable of very short questions and answers. However, as of 2026, "complex tasks" like reading thousands of pages of documents at once or remembering conversation details from months ago have become commonplace. Users expect AI to remember them and understand the full context of their business.

But context cannot be infinitely long. As context length increases, costs grow exponentially, and AI sometimes experiences "information loss," where it misses crucial details amidst a flood of information. Therefore, memory management strategies to decide "what to remember and what to discard" have become a key factor in determining the quality and cost competitiveness of AI services.

Basic Structure of LLM Context Management

Context Window: The maximum number of tokens a model can "see" and process simultaneously in a single inference process. It's like a "working memory" space.
Tokenization: The process of breaking down text into units (tokens) that AI can understand. It's a basic unit that determines how much space is occupied in the context window.
Persistence Layer: An external system that manages information when a conversation exceeds the context window by summarizing past information or saving it to a database (RAG) to be retrieved later.

The core principle is that "every bit of input data serves as a basis for answers, but since space is limited, priority-based management is essential."

Common Misconceptions About LLM Memory

Misconception 1: The larger the context window, the better?

Reality: While a larger space allows for more information, the probability of the "Lost in the Middle" phenomenon—where the model misses key information in the middle of a sentence—increases. Additionally, longer inputs lead to slower Time to First Token (TTFT) and higher costs.

Misconception 2: AI naturally remembers all conversations like a human?

Reality: AI models themselves are "stateless." The appearance of remembering previous conversations is because developers re-input past conversation details with each new question. This process is called "memory management."

Misconception 3: Once information is entered, it stays in context forever?

Reality: As new conversations continue and the context window limit is exceeded, the oldest information is pushed out (FIFO method). Important information (e.g., user name, persona settings) must be managed by pinning it in the "system prompt" or through summarization.

Actual Usage Scenarios

Scenario 1: Specialized Consultation Based on Long Documents

When conducting consultations by putting hundreds of pages of manuals into context, using RAG (Retrieval-Augmented Generation) to search and include only the parts related to the question is much more accurate and economical than including the whole document.

Scenario 2: Long-Term Project Assistant

To have an AI remember previous discussion points in a project lasting several days, a strategy of "summarizing" the key points at the end of each session and including them at the start of the next conversation is effective.

Scenario 3: Personalized Recommendation Service

By formalizing a user's past preference information into a "user profile" and storing it in memory, you can provide optimized answers without having to dig through long conversation histories every time.

Direct Context Input vs. Summarization Memory vs. RAG (Retrieval-Augmented Generation)

Comparison Item	Full Context Input	Summarization Memory	RAG (Search Augmented)
Accuracy	Very High (Short-term)	Moderate (Information loss occurs)	High (Evidence-based)
Cost Efficiency	Very Low (High cost)	Moderate	High (Extract only what is needed)
Processing Speed	Slow	Fast	Moderate (Includes search step)
Suitable Usage	Short, high-density analysis	Maintaining long conversation flow	Leveraging vast knowledge bases

Selection Criteria: If the information to be processed is within 20% of the context window, use direct input; if only the flow of the conversation needs to be understood, use summarization; and if thousands of documents are the target, select RAG.

Executive Execution Summary

Item	Execution Criteria
Deployment Unit	Recommended to operate at 50-70% of the model's maximum context window.
Input Rules	Always place the most important instructions (System Prompt) at the very top or bottom of the context.
Verification System	Check for information loss using the "Needle In A Haystack" test.
Quality Metric	Token consumption efficiency and evidence matching ratio in answers.
Expansion Condition	Transition to RAG when token costs within a single session start exceeding the budget.

Frequently Asked Questions (FAQ)

Q1. What should I do when the context is full?▾

The most common method is the "sliding window" technique, where the oldest messages are removed and new ones are inserted. However, a better way is to compress the entire conversation into a single "state summary" message.

Q2. Are there prompt tips for saving tokens while increasing recall?▾

Reduce unnecessary modifiers and convey information focusing on "keywords." Additionally, including explicit instructions in the system prompt to "refer to [specific information] in subsequent conversations" increases memory efficiency.

Q3. If I use RAG, do I not need to worry about the context window?▾

RAG is a technology for "retrieving" necessary information. For the LLM to read the retrieved information, it still needs space in the context window. Therefore, a design is required to adjust the amount of information retrieved (Top-K) by RAG according to the context size.