Cut our GPT-4 costs by 60% with this hybrid approach - sharing what worked

This is a really cool strategy! I've been using a similar approach with our e-commerce customer service bot. We actually use GPT-3.5 for 80% of interactions, and only switch to GPT-4 for very niche product queries. It’s saved us around 50% in costs so far. Do you find that your classifier ever misroutes queries, or has it been pretty solid?

This is insightful! We've been using a similar routing setup and saw about 50% cost savings. Instead of DistilBERT, we tried using a simple rule-based system to classify queries, which brought our costs down slightly more since it required less compute. However, I'm intrigued by the use of a lightweight model for classification as it likely improves accuracy. Might give that a shot!

Thanks for sharing! We venture a little differently by opting for Google's PaLM API for complex queries due to slightly cheaper token rates. Mixing that with GPT-3.5-turbo has been pretty effective on our end; however, the language nuances from GPT-4 do make a slight difference in quality. Have you compared these models side-by-side by any chance?

Nice work on the prompt engineering - cutting tokens in half is huge. We tried Claude-2 for a similar use case and honestly the routing complexity wasn't worth it. Claude's pricing is competitive enough that we just use it for everything now. Running about $900/month vs your $780 but zero routing headaches and the quality is consistently better than 3.5-turbo for our support queries.

This is brilliant! We're doing something similar but with a simpler rule-based router (keyword matching + intent confidence scores). Getting about 70% to GPT-3.5 but your DistilBERT approach sounds way more sophisticated. How much training data did you need for the classifier? And are you handling edge cases where 3.5 fails and you need to retry with GPT-4?

Great insight into optimizing model use! I've done something similar but instead of using GPT-3.5-turbo, I've been experimenting with Cohere's Command R and it’s been quite efficient as well. We saw about a 50% drop in costs and similar satisfaction levels. Might be worth looking into if you're shopping for alternatives.

Interesting approach! Did you face any challenges with the fine-tuned DistilBERT classifier misrouting queries? Also, how did you decide which queries were 'simple' versus 'complex'? Would love to hear more about that classification criteria.

I've had a similar experience! We switched to a hybrid model with GPT-3.5 for FAQs and used GPT-4 only for more nuanced queries. Didn't think about using DistilBERT as a classifier though; we still manually tagged queries. Might need to try that out!

I'm curious about your experience with prompt engineering. You mentioned reducing token usage significantly; any specific techniques you found most effective for trimming down your prompts? We've been struggling with prompt length too and any insights would be awesome!

I've been using a similar setup but with Claude from Anthropic for some of our queries. Pricing-wise, it’s slightly higher than GPT-3.5-turbo, but I found it does slightly better with nuanced language, especially with technical jargon. Still, your use of Redis for caching is genius! I'll have to implement a similar caching strategy since a lot of our incoming queries are repetitive as well. Thanks for the tip!

I've been thinking about a similar approach, but using Azure OpenAI's service. Anyone have experience with pricing there? Wondering if server costs might offset the savings.

We did something similar with a mix of GPT-3.5 and older models like GPT-3 for super basic queries. Managed to cut costs by about 50%, but your setup with the classifier is really interesting. Do you find any delay with the classifier decision making, or is it pretty seamless?

Great insights! We've used a similar prompt engineering strategy, reducing our token count significantly. Dropped from an average of 900 tokens to 500 on our customer service bot without losing essential context. Our costs decreased around 55%, maintaining user satisfaction around 4.1 out of 5. Love hearing real-world applications of prompt optimization!

Impressive savings! I've been hesitant to mix models for our support bot but your metrics are convincing. I'm curious about the classifier—how did you handle edge cases where it wasn't certain about the complexity?

We've been able to reduce costs by about 50% using a similar strategy, though instead of prompt engineering, we focused on improving our history management. We've configured our system to only pass on new or non-redundant information to the models, which lowered our token usage quite a bit. We're spending around $900/month now compared to over $1,800 before.

I've had success with a lightweight ensemble model approach—using three different language models, including an open-source one for the simplest queries. While it complicated the setup, our costs dropped by 50% and it added flexibility for future AI model integrations.

We've been using the Anthropic Claude model for simple queries because we found it a bit cheaper on our traffic profile. We paired it with a BERT-based classifier like you suggested, and our costs came down by around 50% with little impact on the quality metrics. Worth checking out their pricing if you're considering alternatives!

We've implemented a similar strategy but used a rule-based engine for simple queries instead of a DistilBERT classifier. This reduced our training costs immensely, and we're paying less than $500 now. However, it's not as flexible for evolving queries. Anyone found a sweet spot balancing both?

I've been using a similar method but with an extra layer of rule-based logic before sending queries to AI models. This helps to filter out the simplest questions entirely without reaching the ML models. We also saw a drop in our costs by around 50%, though our setup cost was a bit higher due to the initial rule-base development.

Great breakdown! We've been trying something similar but using LangChain for the routing logic instead of a direct DistilBERT classifier. It offers more flexibility with integrating various models. I'm curious though, how did you handle tracking when to escalate a query to GPT-4? Was there a specific threshold or a score from DistilBERT?

This is a great approach! I started using a similar method but went with the Huggingface API for the classifier and it worked well too. However, I'm curious if anyone has tried using a cheaper language model than GPT-3.5-turbo for the simple queries and what their experiences were.

Great insight on using DistilBERT for routing! I've been toying with BERT-based classifiers too, although I've found RoBERTa to be slightly more accurate but a bit more costly to run. Curious, did you explore any fallback mechanisms for when your classifier misroutes a query? It's something I've been considering implementing.

I'm curious about the aggressive prompt engineering you mentioned. Our team's been struggling to keep token usage low without sacrificing response quality. Can you share some specific techniques or examples that helped reduce the tokens used in responses?

We've also been leveraging GPT-3.5-turbo for initial query handling. Our results weren't as cost-effective at first, but after integrating a similar classifier using BERT, our monthly expense dropped by 55%. We didn’t track user satisfaction before implementing the change, though—good to see your drop was minimal!

What led you to choose DistilBERT for your classifier? I’ve been contemplating using a simpler architecture like logistic regression if the feature set is carefully selected, especially for straightforward classification tasks. Wondering if the added complexity of DistilBERT is really worth it in your scenario.

Great results! Could you share more about the prompt engineering techniques you implemented to reduce token usage? We're struggling to get ours below 700 on average, and any tips would be super helpful.