![wk3-l3a-memory-strategies-hero.jpeg](wk3-l3a-memory-strategies-hero.jpeg) --- [🏠 Home - All Lessons](https://app.readytensor.ai/hubs/ready_tensor_certifications) [⬅️ Previous - System Prompts](https://app.readytensor.ai/publications/t79Iyg4lva2t) [➡️ Next - Memory Persistence](https://app.readytensor.ai/publications/Ax1KnCSIt9QI) --- :::info{title="Code Implementation + Live Testing"} This lesson has a practical video demonstration at the end that shows how to test and compare different memory strategies (stuffing, trimming, summarization) in a real conversation simulation. We recommend reading through the lesson first to understand the concepts, then watching the video to see how these memory strategies actually impact token usage in practice. ::: # TL;DR In this lesson, you'll learn how to manage conversation memory when chat sessions get long. We'll explore three strategies - stuffing everything, trimming to recent messages, and summarizing conversation history - and see how each affects your prompts, costs, and user experience. # Problem: The Cost of Unlimited Chat History 🚨 In our previous lessons, we built a multi-turn conversation system by simply **stuffing each user/AI interaction into our chat history** and passing the entire thing to the LLM. This works great for short conversations, but what happens when things get longer? Let's think about a realistic scenario for technical AI assistants: imagine a user having an in-depth conversation about the VAE publication, exploring each of the 5 use cases in detail. Suppose each user-AI interaction adds 500-1,000 tokens to the conversation history. After just 10 turns, you're looking at 5,000-10,000 tokens of history. After 20 turns, that's 10,000-20,000 tokens. After 50+ turns of conversation, we're looking at some serious problems: **⚠️ Token Limits** Most models have context limits (4k, 8k, 32k tokens). A long conversation can easily hit these limits, causing your application to break entirely. **💰 Cost Explosion** With 50 turns, you're sending 50,000-100,000 tokens of history with every new request. That's expensive and inefficient. **🧠 Context Pollution** Early parts of the conversation become irrelevant noise as chat session gets longer. The LLM has to process all that old context, potentially leading to confused or unfocused responses. **🐌 Performance Issues** Processing massive context windows is slower and more resource-intensive, leading to poor user experience. So what should we do? Let's explore three different memory management strategies. # Three Memory Management Strategies ![memory-strategies.webp](memory-strategies.webp) ## 1️⃣ **The “Stuff Everything In” Strategy** This is our current approach: keep every single message in the conversation history. ```txt System Prompt: You are a helpful research assistant... User Turn 1: Tell me about VAE applications AI Assistant Turn 1: VAEs have 5 major applications including data compression, noise reduction... User Turn 2: What about data compression specifically? AI Assistant Turn 2: For data compression, VAEs work by learning efficient representations... User Turn 3: Can you explain the encoder-decoder architecture? AI Assistant Turn 3: The encoder maps input data to a latent distribution... ... User Turn 46: How is it used for missing data imputation? AI Assistant Turn 46: For missing data imputation, VAEs can reconstruct incomplete samples... ``` This is what we’ve been doing so far: ✅ Simple to implement ❌ Doesn’t scale for long chats ❌ Expensive and can lead to hallucination You can implement this with LangChain’s **ConversationBufferMemory**: ```python from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationChain memory = ConversationBufferMemory(return_messages=True) conversation = ConversationChain(llm=llm, memory=memory, verbose=True) ``` --- ## 2️⃣ **Trim Older Messages (Sliding Window)** A common approach is to only keep the most recent "N" messages — like the last 3–5 turns. This is called a sliding window. ```txt System Prompt: You are a helpful research assistant... User Turn 44: Explain anomaly detection. AI Assistant Turn 44: Anomaly detection with VAEs works by... User Turn 45: And noise reduction? AI Assistant Turn 45: For noise reduction, VAEs filter... User Turn 46: What about missing data imputation? AI Assistant Turn 46: VAEs can reconstruct incomplete samples... ``` This approach: ✅ Reduces prompt size ✅ Keeps context recent and relevant ❌ May lose important older context if the user switches topics back LangChain’s **ConversationBufferWindowMemory** makes this easy: ```python from langchain.memory import ConversationBufferWindowMemory memory = ConversationBufferWindowMemory(k=3, return_messages=True) conversation = ConversationChain(llm=llm, memory=memory, verbose=True) ``` --- ## 3️⃣ **Summarize or Refine Older History** The most advanced (and often best) approach is to summarize older parts of the conversation — keeping key points, not every word. Example summarized conversation history: ```txt System Prompt: You are a helpful research assistant... Summary of earlier conversation: - Discussed 5 use cases of VAEs: anomaly detection, noise reduction, data compression, synthetic data generation, and missing data imputation. - User wanted deep explanations of data compression and anomaly detection. User Turn 45: Tell me about noise reduction. AI Assistant Turn 45: For noise reduction, VAEs filter... ``` ✅ Retains essential context while shrinking prompt size ✅ Works well for focused, long conversations ❌ Summary generation adds complexity and cost ❌ May lose subtle details in summarization This strategy can be implemented with LangChain’s **ConversationSummaryMemory**: ```python from langchain.memory import ConversationSummaryMemory memory = ConversationSummaryMemory(llm=llm, return_messages=True) conversation = ConversationChain(llm=llm, memory=memory, verbose=True) ``` --- # Protecting Your System Prompt 🔍 No matter what memory strategy you use, remember: > **The system prompt should always be retained** — it’s your AI’s North Star. **Frameworks like LangChain handle this automatically** (the system prompt is always kept separately from conversation memory). So you can focus on managing **user and assistant turns** without worrying about losing the AI’s core behavior and role. --- # Hands-On Experiment: Comparing Memory Strategies 🧪 To see how these strategies affect **prompt size and performance**, we’ve prepared a demonstration script: ✅ It simulates a long conversation using the **VAE publication questions**. ✅ It runs each memory strategy and saves the final prompt and response. ✅ It also saves a **comparison report** showing how many tokens each strategy uses! **Watch the live experiment here: 👉** :::youtube[Simulating Memory Strategies: How Conversation History Affects Token Usage]{#ZRMatEAT9bM} This demo will give you a practical feel for how conversation memory shapes your AI’s behavior — and why understanding token usage is crucial for long-term cost and performance. --- :::caution{title="Caution"}

What About Response Quality?

This lesson focused on token usage and performance considerations. We didn't evaluate response accuracy or quality across strategies - that's a topic we'll cover in Module 2 when we dive into evaluation methods for agentic AI systems. ::: # Key Takeaways ✅ **Memory strategies make or break long conversations** - choose based on your use case ✅ **Significant token savings** are achievable with smart memory management ✅ **Different strategies affect response quality**, not just costs --- [🏠 Home - All Lessons](https://app.readytensor.ai/hubs/ready_tensor_certifications) [⬅️ Previous - System Prompts](https://app.readytensor.ai/publications/t79Iyg4lva2t) [➡️ Next - Memory Persistence](https://app.readytensor.ai/publications/Ax1KnCSIt9QI) ---