Strategies to maintain dialogue context

When you build an AI assistant, answering one-off questions is easy. But real conversations don’t happen in isolation — people expect your assistant to remember what they just said, handle follow-ups, and respond naturally in a multi-turn dialogue.

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✅ Why Context Matters

Without context, your assistant:

• Forgets earlier parts of the chat.

• Misunderstands follow-up questions (“What about its price?” — What is “it”?).

• Produces answers that feel robotic and disconnected.

A good assistant keeps track of conversation history so its replies stay coherent, relevant, and human-like.

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✅ Core Challenge

Most open LLMs (like Llama‑2, Mistral, or Bloom) don’t have built-in memory. So you, the developer, must:

• Store the chat history.

• Feed the right parts of that history back to the model every turn.

This is called context window management.

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✅ Common Strategies

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1️⃣ Simple Prompt Concatenation

Most common and easiest:

• Keep a rolling list of the last N turns.

• Concatenate them into a single prompt each time.

Example:

[System]: You are a helpful assistant.
[User]: What’s Python?
[Assistant]: Python is a popular programming language...
[User]: Who created it?

You build a single text prompt:

You are a helpful assistant.

User: What’s Python?
Assistant: Python is a popular programming language...
User: Who created it?
Assistant:

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✅ Pros: ✔️ Easy to implement. ✔️ Works well for short chats.

❌ Cons: ❌ Limited by model’s max token limit (context window). ❌ History can get cut off if the chat is too long.

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2️⃣ Sliding Window

When conversations get long:

• Keep only the last N messages that fit your token budget.

• Drop older turns.

Example: Keep the last 5–10 exchanges.

✅ Pros: ✔️ More stable for longer sessions. ✔️ Controls prompt size.

❌ Cons: ❌ May lose important background info.

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3️⃣ Summarized Context

Use the model itself to summarize older chat:

• Keep the full last few turns.

• Replace older turns with a short summary.

Example:

[Summary]: User wants to learn Python basics.
User: Tell me about variables.
Assistant: Variables are containers for values...
User: Show me an example.

✅ Pros: ✔️ Fits more history in fewer tokens. ✔️ Keeps relevant facts alive.

❌ Cons: ❌ Summaries can lose detail or misrepresent.

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4️⃣ Session IDs + External Memory

Store chat history in a database:

• Keep user sessions separate.

• Load only relevant parts per user.

For advanced setups, pair this with:

• RAG: Store and retrieve facts the user shares during chat.

• Custom state store: Save structured info (user preferences, facts, tasks).

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✅ How to Implement in Code

In your Gradio or FastAPI backend:

• Use a list or dict to store [(user_input, assistant_response)].

• Build each new prompt by joining history.

• Clear or trim the list when it grows too large.

Example:

history = []

def chat(user_input):
    # Add user message
    history.append(("User", user_input))

    # Build prompt
    prompt = "You are a helpful assistant.\n\n"
    for role, text in history:
        prompt += f"{role}: {text}\n"
    prompt += "Assistant:"

    # Generate
    response = generator(prompt, ...)

    # Add assistant reply to history
    history.append(("Assistant", response))

    return response

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✅ Tips for Good Context

✔️ Always prefix roles: User:, Assistant: ✔️ Use consistent style: same system prompt every time. ✔️ Trim history carefully — too much = high cost, too little = bad coherence. ✔️ Consider safety: never leak user data between sessions.

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🗝️ Key Takeaway

A good dialogue flow = good context. Stitch your turns into the prompt wisely so your assistant stays coherent, helpful, and human-like — even in longer chats.

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➡️ Next: Learn how to handle token limits and test your assistant’s multi-turn consistency!