Why You Cannot Solely Rely on GPT-4 for Production-ready Chatbots?
Ritesh Kanjee
Making Business Easier with AI. High Accuracy GenAI RAG & Computer Vision Expert, Consultant Developer - Over 17 years experience in AI, ML and CV.
I won't lie, building chatbots is hard!
When I first embarked on this journey, I naively believed that simply calling the GPT-4 API and stringing prompts together would suffice for managing customer interactions
What a reality check that was...haha??!
Through my experimentation, I've pushed the boundaries of GPT-4's capabilities while learning some tough lessons along the way.
Curious about these boundaries?
The first thing to grasp is that no matter how cleverly you concatenate prompts, a Large Language Model (LLM) like GPT-4, Gemini, or LLaMA 2, it just won't cut it.
These models lack the nuanced understanding of your unique business landscape, your product intricacies, and the essence of your company culture.
Even if you attempt to spoon-feed all the necessary data through prompts, the AI often becomes muddled and spits out irrelevant or hallucinated responses.
Why is that?
The inherent flaw in LLMs is that the longer your prompt, the more it struggles to maintain coherence with your instructions.
So, what's our strategy to navigate these limitations?
We could fine-tune our model, though it's not without its hurdles.
Hurdles such as structuring your data correctly
Alternatively, you could turn to LangChain...
LangChain, you wonder? What on earth is that?
LangChain, my friend, is a framework designed for Retrieval Augmented Generation. You've probably seen the acronym RAG tossed around online.
And RAG, in simple terms, is like a bridge that connects your Data, Documents, and Web search results with your LLMs, whether you opt for GPT-4 Turbo, Gemini, Mistral, and so on.
Now, how do we harness LangChain (LLMs with RAG) for our chatbots?
By leveraging the LangChain framework, we've been able to integrate:
These resources were instrumental in refining our chatbot
These are just some of the documents that we used for tweaking our chatbot to ensure the most optimal journey for our potential client.
Together with our special system prompts, we managed to get to about 82% user satisfaction rating for people interested in our products!
领英推荐
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Amazing right!?
Im intrigued! What are these special prompts and how can I start using LangChain?
I'm thrilled you're on board ;). We're bringing back our LangChain Chatbots Pro course by popular demand.
You'll be building over 15 LangChain Apps with Streamlit for various industrial sectors.
But here is the best part!
We are including the LangChain Chatbots Pro Course in Augmented AI University Membership.
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Here's a review by one of our students who have taken our other courses:
Immediately Hiring | Dot net developer | Project coordinator | Angular Developer | React Native Developer | Technical Project Manager
11 个月Thanks for posting
Building chatbots is definitely challenging, but with your dedication and expertise, you're sure to excel in optimizing their performance! Your insights on the importance of LangChain are truly invaluable in elevating the chatbot experience. ??????
Data Scientist | Machine Learning Engineer | Computer Vision Engineer | NLP | LLM
11 个月How can I incorporate my tables to prompt using RAG?
Executive Engineering Leader | Board Member | AI-First | Champion of Software Engineering, AI, Cloud, Python & Serverless | Mentor | Strategic Innovator | AWS Community Builder
11 个月Insightful read, Ritesh! Your exploration of the limitations of GPT-4 in chatbot development underscores the importance of understanding the nuances of AI tools. It's a great reminder that there's no one-size-fits-all solution in AI.