Impact of LLMs on the Tech Stack and Product Development // Anand Das // #188
Nov 7, 2023
auto_awesome
Anand Das, co-founder and CTO of Bito, discusses the impact of LLMs in tech stack and product development, including their Chrome extension 'explain code'. They explore topics such as optimizing at lower levels, ensuring privacy in non-open source projects with AI language models, and best practices for debugging with coding assistants and learning models.
Different APIs offer varying strengths and limitations, and it is crucial to choose the most suitable API based on the task and input size.
Tailoring prompts for different language models, such as GPT-3.5, GPT-4, and anthropic, is essential to optimize interactions with AI coding assistants.
Providing additional context, like code snippets or exception information, can improve the accuracy of AI coding assistant responses and the presence of experienced developers who understand the codebase is beneficial.
Deep dives
Using multiple APIs for scale and variety
The podcast episode highlights the use of multiple APIs, such as OpenAI, Azure, and anthropic, to handle the scale and variety of AI coding assistant tasks. The guest speaker from B2 discusses how they leverage different APIs based on the size of input and the specific operation being performed. These APIs offer different strengths and limitations, and the speaker emphasizes the need to carefully choose the most suitable API for each scenario.
The importance of tailored prompts
The conversation emphasizes the significance of using tailored prompts for different language models. The guest speaker mentions the need to create specific prompts for GPT-3.5, GPT-4, and anthropic models to maximize their effectiveness. Each model responds differently to prompts, and customization plays a key role in obtaining accurate and relevant answers. By fine-tuning prompts for each model, users can optimize their interactions with AI coding assistants.
Managing limitations and debugging with context
The podcast discusses the limitations of AI coding assistants and the importance of providing sufficient context for better results. The guest speaker suggests that developers should be aware that AI models might not know everything and could provide inaccurate or hallucinated answers. By providing additional context, such as specific code snippets or exception information, developers can improve the accuracy of the responses. The speaker also mentions the importance of experienced developers who understand the codebase, as they can ask more precise questions and derive better outcomes.
Considerations for using GPUs versus API services
The episode explores the cost-benefit analysis between using GPU resources versus relying on API services for AI coding assistance. The speaker explains that for startups and smaller scales, using API services proves more cost-effective and manageable, as it avoids the expenses associated with maintaining GPU infrastructure and managing high availability. However, for enterprises or at a larger scale, using in-house GPU resources might become more viable. The decision heavily depends on factors such as cost, availability, and specific requirements of the organization.
The ongoing need for prompt engineering and testing
The episode highlights the importance of prompt engineering and continuous testing to enhance the performance of AI coding assistants. The speaker emphasizes the need to develop tailored prompts for different language models and regularly test and tweak the prompts to achieve desired outcomes. They also discuss the challenges of effective prompt testing and the need to display caution while verifying results. The discussion suggests that prompt-driven development and ongoing prompt optimization are essential for maximizing the benefits of AI coding assistants.
MLOps podcast #188 with Anand Das, Co-founder and CTO of Bito, Impact of LLMs on the Tech Stack and Product Development.
// Abstract
Anand and his team have developed a fascinating Chrome extension called "explain code" that has garnered significant attention in the tech community. They have expanded their extension to other platforms like Visual Studio code and Chat Brains, creating a personal assistant for code generation, explanation, and test case writing.
// Bio
Anand Das is the co-founder and CTO of Bito. Previously, he served as the CTO at Eyeota, which was acquired by Dun & Bradstreet for $165M in 2021. Anand also co-founded and served as the CTO of PubMatic in 2006, a company that went public on NASDAQ in 2020 (NASDAQ: PUBM).
Anand has also held various engineering roles at Panta Systems, a high-performance computing startup led by the CTO of Veritas, as well as at Veritas and Symantec, where he worked on a variety of storage and backup products.
Anand holds seven patents in systems software, storage software, advertising, and application software.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Website: https://bito.ai/
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Anand on LinkedIn: https://www.linkedin.com/in/ananddas/
Timestamps:
[00:00] Anand's preferred coffee
[00:15] Takeaways
[02:49] Please like, share, and subscribe to our MLOps channels!
[03:08] Anand's tech background
[10:06] Fun at Optimization Level
[12:59] Trying all APIs
[17:55] Models evaluation decision tree
[22:51] Weights and Biases Ad
[25:04] AI Stack that understands the code
[28:27] Tools for the Guard Rails
[33:23] Seeking solutions before presenting to LLM
[38:46] Prompt-Driven Development Insights
[40:16] Prompting best practices
[42:51] Unneeded complexities
[45:45] Cost-benefit analysis of buying GPUs
[49:13] ML Build vs Buy
[51:26] Best practices for debugging code assistant
[54:58] Wrap up
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
