How AI Is Built 🛠

AI agents might soon be running your business workflows? Just wrapped up a podcast on the future of AI agents. 5 Key Takeaways: - The "human in the loop" is evolving: It's no longer about offshore workers completing tasks, but about AI pausing to ask YOU for critical decisions. - Cost vs. Accuracy Trade-off: For high-stakes tasks (think financial analysis), accuracy trumps cost. But for marketing copy? A faster, cheaper model might suffice. - The Workflow Capture Challenge: The magic often happens in the user's head. Capturing that decision-making process is the next frontier in agent development. - Enterprise-Ready Agents: Deploying AI agents in corporate environments requires robust security, scalability, and integration with existing systems. - The Future is Declarative: Moving beyond chat interfaces, the next big leap could be agents that understand and modify system states directly. Learn more about how you can actually bring agents into production and listen to the latest episode of How AI Is Built 🛠 with Rahul Parundekar, AI Hero. Links are in the comments below. #aiagents #agents #llms

Are you facing these reliability nightmares with your AI agents? - Does your AI agent occasionally produce nonsensical or off-topic responses? - Are you struggling to maintain consistent output quality as your agent scales? - Do minor changes in input sometimes lead to wildly different outputs? (1) Prompt Engineering for Consistency Implement structured prompts with clear instructions and examples Use few-shot learning techniques to guide the model's behavior Regularly update and refine prompts based on performance data (2) Output Filtering and Post-processing Implement content filters to catch inappropriate or off-topic responses Use language models or rule-based systems to refine and polish agent outputs Implement fact-checking mechanisms for critical information (3) Prompt Preprocessing and Compression Reduce token count without losing essential information Improve consistency by focusing on key elements of the prompt Enable use of larger context windows without increased costs In today's podcast of How AI Is Built 🛠, Richmond Alake gives you the tools to create more reliable and predictable AI agents. Richmond is an AI engineer at MongoDB working on using agents to solve real-world problems. We talk about some of the latest tools for LLM and AI Agents like Microsoft's LLMLingua (Huiqiang Jiang) for prompt compression, crewAI (João (Joe) Moura) and LangChain (Harrison Chase) for building agents, and more. Catch the episode on Spotify, Apple, Snipd, or wherever you get your podcasts. Links are in the comments below! What reliability challenges have you faced with your AI agents? Let me know below! Catch you next week with another episode in our series on AI agents with Rahul Parundekar.

It's often beneficial to start with a simplified version of these concepts and iterate as you learn more about your specific integration challenges.

We had the pleasure of hosting Derek Tu of Carbon. If you want to learn how to feed your data-hungry friends at OpenAI or Anthropic, listen to this one. For the ones who do not want to listen to 37 minutes of me learning from Derek, the gist is below. https://lnkd.in/dperWzTh

When building LLMs, we easily get caught up with the new, shiny optimizations: optimizing chunking strategies and fine-tuning prompts. The biggest gains can often be found in the pipes. Improving the data that is fed into your models. Tags, such as topics, the source of the document, and other metadata, allow you to retrieve more relevant input and contextualize it for the LLM. In today's episode of How AI Is Built 🛠, I talked to Derek Tu, founder of Carbon. Carbon is an ETL tool specifically built for LLMs that connects to various data sources and parses and chunks unstructured data before delivering it to a vector database. Beyond tagging, we discussed fine-tuning embedding models, necessary innovations to drive the space forward, building ETL pipelines for LLMs, extracting data from documents, and much more. Jump into the podcast ⬇ (comments) Stay tuned for next week when we start a little mini-series on agents with Richmond Alake. #llms #genai #etl

Learn how to orchestrate tasks like data ingestion, transformation, and AI calls, as well as how to monitor and get analytics on data products.

Data storage costs skyrocketing? Quantization offer a powerful, affordable solution. Embeddings can be big, taking up lots of space. That's where quantization comes in. It's shrinking the embeddings down, making them easier to store and search through. Two popular methods are binary quantization (BQ) and product quantization (PQ). BQ simplifies embeddings, making them smaller but still useful. PQ breaks them into pieces, compressing each part separately. Learn more in today's podcast on How AI Is Built with Zain Hasan from Weaviate. https://lnkd.in/dZqwP4tG Let me know below what use-cases you dream about with low-cost storage. #rag #vectordatabase #llm #generativeai

Ever wondered how AI systems handle images and videos, or how they make lightning-fast recommendations?

Have you ever wondered how complex AI and data systems are built? For any AI or data application, you have to make a lot of choices. 1. What type of storage to use. 2. How to extract data from its sources. 3. How to orchestrate your pipelines 4. How to integrate AI into data processing. In this episode of How AI Is Built, Anjan Banerjee shares his expertise on identifying data sources, selecting the right tools for extraction and storage, and the growing popularity of multi-modal storage engines like Snowflake, Databricks, and BigQuery. We also discuss the pros and cons of data orchestration tools like Airflow and the importance of choosing the right solution based on your organization's technical capabilities. Key takeaways: - Native cloud services can sometimes outperform third-party tools - TinyML is making waves in manufacturing and industrial setups - "Poka-yoke" error-proofing is crucial for data quality - Snowflake is overhyped, while Databricks shines for heavy data processing Listen to the full episode here: https://lnkd.in/dVfhVkj6 Questions for you: - What are your thoughts on the rise of multi-modal storage engines? - How do you ensure data quality and standardization in your AI and data systems? Share your experiences and insights in the comments below! #AI #dataengineering #data

From starting with the right data sources to implementing robust data orchestration and ensuring data quality, Anjan provides a comprehensive guide to navigating the challenges of designing data architectures for AI. 💪