Updating AI Workflows for Latest LLM Releases

Do LLMs keep giving you outdated code examples? That's not their fault; they can't read docs from 2025. But with modern tooling, building a system that keeps LLM agents synced... The problem I kept hitting I work at Weaviate, and our Python client syntax is regularly updated with new features and improvements. But LLMs' training data are frozen in time. Developers ask for code examples and get confident responses with syntax from 2023 that no longer works. So this weekend, I hacked something together to show you how to fix it. The stack I put together: - Crawl4AI scrapes documentation sites - Weaviate stores chunked content with embeddings - #MCP server exposes search as a Claude tool - Pydantic AI builds agents that query live docs The result? Claude searches current documentation and cites sources. Every code example reflects the latest syntax. I started with Weaviate's docs since I know them well, but the pattern generalizes to any evolving documentation: → Internal company wikis → API docs (Stripe, Twilio, AWS, whatever) → Framework docs that change with each release → Any knowledge base that needs to stay current The whole project is under ~200 lines of code, split into 5 files. Check it out! https://lnkd.in/ecJC3m6C

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe

Excited to share our end-to-end LLM workflows guide that we’ve used to help our industry customers fine-tune and serve OSS LLMs that outperform closed-source models in quality, performance and cost. Key LLM workloads with docs.ray.io and Anyscale: - 🔢 Preprocess our dataset (filter, schema, etc.) with batch data processing. - 🛠️ Fine-tune our LLMs (ex. Meta Llama 3) with full control (LoRA/full param, compute, loss, etc.) and optimizations (parallelism, mixed precision, flash attn, etc.) with distributed training. - ⚖️ Evaluate our fine-tuned LLMs with batch inference using Ray + vLLM. - 🚀 Serve our LLMs as a production application that can autoscale, swap between LoRA adapters, optimize for latency/throughput, etc. Key Anyscale infra capabilities that keeps these workloads efficient and cost-effective: - ✨ Automatically provision worker nodes (ex. GPUs) based on our workload's needs. They'll spin up, run the workload and then scale back to zero (only pay for compute when needed). - 🔋 Execute workloads (ex. fine-tuning) with commodity hardware (A10s) instead of waiting for inaccessible resources (H100s) with data/model parallelism. - 🔙 Configure spot instance to on-demand fallback (or vice-versa) for cost savings. - 🔄 Swap between multiple LoRA adapters using one base model (optimized with multiplexing). - ⚡️ Autoscale to meet demand and scale back to zero. 🆓 You can run this guide entirely for free on Anyscale (no credit card needed). Instructions in the links below 👇 🔗 Links: - Blog post: https://lnkd.in/gvPQGzjh - GitHub repo: https://lnkd.in/gxzzuFAE - Notebook: https://lnkd.in/gmMxb36y

LLMOps is becoming the new DevOps for AI engineers. Getting a prompt to work is the easy part. The real challenge is making your LLM applications repeatable, scalable, and reliable in production. That’s where LLMOps comes in. Think of it as the operating system for LLM-driven applications, from data prep to responsible deployment. Here are the core components of an LLMOps pipeline (see diagram 👇): ➡️ Model Customization: data preparation, supervised fine-tuning, evaluation ➡️ Behind the Scenes: foundation + fine-tuned models, pre-processing, grounding with external knowledge, post-processing with responsible AI filters ➡️ LLM Response Layer: prompting, user interaction, and outputs ➡️ Pipelines: orchestration (data versioning, configs, workflow design) and automation (deployment, execution, monitoring) As engineers, the craft isn’t just in building the model, it’s in building the system around the model. 💡 Here are some excellent repos/resources to explore: 👉 Prompt orchestration & pipelines → Haystack, LangGraph 👉 Evaluation & Responsible AI → Ragas, LlamaIndex evals 👉 Data prep & tuning → OpenPipe, Axolotl 👉 Deployment → vLLM, Ray Serve, Fireworks AI If you’re building production-grade AI, don’t stop at the model. Learn to think in terms of LLMOps pipelines- orchestration, automation, and continuous improvement. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg