Contextual Conversational Retrieval for Message-Based prompt

Short Course Q&A ChatGPT Prompt Engineering for Developers
1

My question is, previous message-based results by passing the roles of assistant and user or the entire conversation history.

2

Hi,

In the course, the entire conversation history is passed to the model by means of the context variable.

3

Hi,
Should we pass the whole context considering the size of context might increase as the chat continues? Is there any recommendation to send the number of messages (context)

4

Hi tsfahmad360,

To stay within the input size with a continued chat, the context can be provided in the form of a summary of the preceding chat. Another possibility is to develop an agent. You can see how to do this in the courses on langchain.

5

Contextual Conversational Retrieval (CCR) for message-based prompts is an essential technique in modern messaging app development, especially when aiming to build intelligent, responsive, and context-aware chat systems.

Essentially, CCR allows a system to understand and retrieve relevant information based on the entire conversational history, rather than just a single message or prompt. This is crucial for message-based applications where users often reference previous topics, use pronouns, or change context mid-conversation.

For example, in a chatbot embedded in a messaging app, if a user says:

“Can you show me the files from last week?”

A simple retrieval system might not know what “files” or “last week” refers to unless it understands the previous messages. Contextual retrieval enables the system to analyze the chat history, extract entities and time references, and return results that match the ongoing dialogue.

In messaging app development, implementing CCR involves:

  • Maintaining a conversation memory or session history.

  • Using retrieval-augmented generation (RAG) or dense retrieval models like DPR or ColBERT.

  • Applying natural language understanding (NLU) techniques to capture user intent and context.

From a development perspective, integrating CCR improves user satisfaction, especially in customer support bots, AI assistants, or any feature that relies on natural, back-and-forth communication.

If you’re building a messaging app, I highly recommend looking into frameworks that support contextual embeddings, such as Haystack, LangChain, or OpenAI’s function calling with memory.