During the development of the LLAMA2 Medical Chatbot, one of the primary challenges was optimizing performance on systems without GPU support. Since many healthcare providers may not have access to high-end GPUs, we needed to ensure that the chatbot could run efficiently on CPU-only setups. Using a quantized version of the LLAMA2 model helped reduce the computational load, but integrating it with FAISS for vector search was tricky. The challenge was to maintain low latency while processing complex queries in real-time.

Another issue was related to data processing. Medical documents are often lengthy and unstructured, requiring significant pre-processing. We faced difficulties ensuring that the chunking and embedding process didn’t lose the contextual meaning of the text. By fine-tuning our text-splitting approach and leveraging Sentence Transformers, we were able to improve the quality of embeddings while preserving context.

Lastly, ensuring the chatbot provided safe and accurate responses to medical queries was a critical concern. To overcome this, we implemented a robust prompt system that enforced constraints on the model's responses, reducing the likelihood of hallucinations and focusing on delivering reliable information.