Q&A, Comments, Suggestions
Chatbots are widely used to provide instant answers, but what happens when we need a chatbot that only responds based on specific documentation? For example:
• A chatbot that answers questions about a company’s code of conduct.
• A chatbot that provides details about a product catalog.
• A chatbot that explains technical documentation.
In these cases, the chatbot should only provide answers based on the given documents. If the answer is not available, it should simply say, "I don’t know."
There are three common AI approaches to solving this:
1) Using documents as a context in the prompt
How it works: The documents are inserted directly into the LLM’s prompt, and the model is explicitly instructed to answer only based on this information.
Steps to implement:
1. Select relevant sections of the document (since prompts have token limits).
2. Format the text and add clear instructions to the LLM.
3. Send the prompt to the model and retrieve the response.
Pros: ✅ Easy to implement—no additional system needed. ✅ Works well for short documents or FAQs.
Cons: ❌ Limited by token constraints—large documents may not fit. ❌ The model might still “hallucinate” and provide incorrect answers.
2) Retrieval-Augmented Generation (RAG)
How it works: The system retrieves the most relevant sections from the document database and appends them to the LLM’s prompt before generating a response.
Steps to implement:
1. Store documents in a vector database (e.g., FAISS, Pinecone, Weaviate).
2. Use embedding models to convert document text into numerical representations.
3. When a user asks a question, the system retrieves the most relevant text chunks.
4. The LLM generates an answer based on the retrieved information.
Pros: ✅ Can handle large documents—only relevant parts are retrieved. ✅ More efficient and scalable than inserting entire documents into prompts.
Cons: ❌ Requires additional infrastructure (vector database, retrieval mechanism). ❌ Performance depends on the quality of embeddings and retrieval accuracy.
3) Fine-Tuning a specialized LLM
How it works: Instead of using external retrieval, we fine-tune the LLM by training it with the specific documentation.
Steps to implement:
1. Collect a dataset of questions and answers based on the documentation.
2. Fine-tune an open-source model (e.g., Llama 3, Mistral) with this dataset.
3. Deploy the fine-tuned model to handle queries.
Pros: ✅ The model directly learns the document’s content, reducing reliance on external data retrieval. ✅ Faster response time since no retrieval is needed.
Cons: ❌ Requires significant data preparation and fine-tuning expertise. ❌ If the document changes, retraining is necessary.
Which approach should you choose?
• Use Prompting if the document is small and you need a quick solution.
• Use RAG if the document is large and constantly updated.
• Use Fine-Tuning if you need a highly optimized chatbot with consistent responses.
Each approach has its trade-offs, and the best choice depends on your specific needs. Have you implemented a chatbot using any of these techniques? Share your experiences in the comments!
