Scaling LLM-Powered Product Features

One of the biggest challenges I see with scaling LLM agents isn’t the model itself. It’s context. Agents break down not because they “can’t think” but because they lose track of what’s happened, what’s been decided, and why. Here’s the pattern I notice: 👉 For short tasks, things work fine. The agent remembers the conversation so far, does its subtasks, and pulls everything together reliably. 👉 But the moment the task gets longer, the context window fills up, and the agent starts forgetting key decisions. That’s when results become inconsistent, and trust breaks down. That’s where Context Engineering comes in. 🔑 Principle 1: Share Full Context, Not Just Results Reliability starts with transparency. If an agent only shares the final outputs of subtasks, the decision-making trail is lost. That makes it impossible to debug or reproduce. You need the full trace, not just the answer. 🔑 Principle 2: Every Action Is an Implicit Decision Every step in a workflow isn’t just “doing the work”, it’s making a decision. And if those decisions conflict because context was lost along the way, you end up with unreliable results. ✨ The Solution to this is "Engineer Smarter Context" It’s not about dumping more history into the next step. It’s about carrying forward the right pieces of context: → Summarize the messy details into something digestible. → Keep the key decisions and turning points visible. → Drop the noise that doesn’t matter. When you do this well, agents can finally handle longer, more complex workflows without falling apart. Reliability doesn’t come from bigger context windows. It comes from smarter context windows. 〰️〰️〰️ 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

As companies look to scale their GenAI initiatives, a significant hurdle is emerging: the cost of scaling the infrastructure, particularly in managing tokens for paid Large Language Models (LLMs) and the surrounding infrastructure. Here's what companies need to know: a) Token-based pricing, the standard for most LLM providers, presents a significant cost management challenge due to the wide cost variations between models. For instance, GPT-4 can be ten times more expensive than GPT-3.5-turbo. b) Infrastructure costs go beyond just the LLM fees. For every $1 spent on developing a model, companies may need to pay $100 to $1,000 on infrastructure to run it effectively. c) Run costs typically exceed build costs for GenAI applications, with model usage and labor being the most significant drivers. Optimizing costs is an ongoing process, and the following best practices would help reduce the costs significantly: a) Techniques, like preloading embeddings, can reduce query costs from a dollar to less than a penny. b) Optimizing prompts to reduce token usage c) Using task-specific, smaller models where appropriate d) Implementing caching and batching of requests e) Utilizing model quantization and distillation techniques f) A flexible API system can help avoid vendor lock-in and allow quick adaptation as technology evolves. Investments in GenAI should be tied to ROI. Not all AI interactions need the same level of responsiveness (and cost). Leaders must focus on sustainable, cost-effective scaling strategies as we transition from GenAI's 'honeymoon phase'. The key is to balance innovation and financial prudence, ensuring long-term success in the AI-driven future. #GenerativeAI #AIScaling #TechLeadership #InnovationCosts #GenAI

One of the most promising directions in software engineering is merging stateful architectures with LLMs to handle complex, multi-step workflows. While LLMs excel at one-step answers, they struggle with multi-hop questions requiring sequential logic and memory. Recent advancements, like O1 Preview’s “chain-of-thought” reasoning, offer a structured approach to multi-step processes, reducing hallucination risks—yet scalability challenges persist. Configuring FSMs (finite state machines) to manage unique workflows remains labor-intensive, limiting scalability. Recent studies address this from various technical approaches: 𝟏. 𝐒𝐭𝐚𝐭𝐞𝐅𝐥𝐨𝐰: This framework organizes multi-step tasks by defining each stage of a process as an FSM state, transitioning based on logical rules or model-driven decisions. For instance, in SQL-based benchmarks, StateFlow drives a linear progression through query parsing, optimization, and validation states. This configuration achieved success rates up to 28% higher on benchmarks like InterCode SQL and task-based datasets. Additionally, StateFlow’s structure delivered substantial cost savings—lowering computation by 5x in SQL tasks and 3x in ALFWorld task workflows—by reducing unnecessary iterations within states. 𝟐. 𝐆𝐮𝐢𝐝𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: This method constrains LLM output using regular expressions and context-free grammars (CFGs), enabling strict adherence to syntax rules with minimal overhead. By creating a token-level index for constrained vocabulary, the framework brings token selection to O(1) complexity, allowing rapid selection of context-appropriate outputs while maintaining structural accuracy. For outputs requiring precision, like Python code or JSON, the framework demonstrated a high retention of syntax accuracy without a drop in response speed. 𝟑. 𝐋𝐋𝐌-𝐒𝐀𝐏 (𝐒𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐰𝐚𝐫𝐞𝐧𝐞𝐬𝐬-𝐁𝐚𝐬𝐞𝐝 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠): This framework combines two LLM agents—LLMgen for FSM generation and LLMeval for iterative evaluation—to refine complex, safety-critical planning tasks. Each plan iteration incorporates feedback on situational awareness, allowing LLM-SAP to anticipate possible hazards and adjust plans accordingly. Tested across 24 hazardous scenarios (e.g., child safety scenarios around household hazards), LLM-SAP achieved an RBS score of 1.21, a notable improvement in handling real-world complexities where safety nuances and interaction dynamics are key. These studies mark progress, but gaps remain. Manual FSM configurations limit scalability, and real-time performance can lag in high-variance environments. LLM-SAP’s multi-agent cycles demand significant resources, limiting rapid adjustments. Yet, the research focus on multi-step reasoning and context responsiveness provides a foundation for scalable LLM-driven architectures—if configuration and resource challenges are resolved.

What is the LLM Mesh AI architecture and why your enterprise may need it? Key highlights include: • Introducing the LLM Mesh, a new architecture for building modular, scalable agentic applications • Standardizing interactions across diverse AI services like LLMs, retrieval, embeddings, tools, and agents • Abstracting complex dependencies to streamline switching between OpenAI, Gemini, HuggingFace, or self-hosted models • Managing over seven AI-native object types including prompts, agents, tools, retrieval services, and LLMs • Supporting both code-first and visual low-code agent development while preserving enterprise control • Embedding safety with human-in-the-loop oversight, reranking, and model introspection • Enabling performance and cost optimization with model selection, quantization, MoE architectures, and vector search Insightful: Who should take note • AI architects designing multi-agent workflows with LLMs • Product teams building RAG pipelines and internal copilots • MLOps and infrastructure leads managing model diversity and orchestration • CISOs and platform teams standardizing AI usage across departments Strategic: Noteworthy aspects • Elevates LLM usage from monolithic prototypes to composable, governed enterprise agents • Separates logic, inference, and orchestration layers for plug-and-play tooling across functions • Encourages role-based object design where LLMs, prompts, and tools are reusable, interchangeable, and secure by design • Works seamlessly across both open-weight and commercial models, making it adaptable to regulatory and infrastructure constraints Actionable: What to do next Start building your enterprise LLM Mesh to scale agentic applications without hitting your complexity threshold. Define your abstraction layer early and treat LLMs, tools, and prompts as reusable, modular objects. Invest in standardizing the interfaces between them. This unlocks faster iteration, smarter experimentation, and long-term architectural resilience. Consideration: Why this matters As with microservices in the cloud era, the LLM Mesh introduces a new operating model for AI: one that embraces modularity, safety, and scale. Security, governance, and performance aren’t bolted on and they’re embedded from the ground up. The organizations that get this right won’t just deploy AI faster they’ll actually deploy it responsibly, and at scale.

Inference stacks for LLMs are fragmenting into specialized layers, each solving distinct pain points in scale, memory, orchestration, and programmability. The emerging picture isn’t about a single best framework but a modular ecosystem you compose to match workload and hardware realities. vLLM demonstrated how memory becomes the real differentiator. With PagedAttention, prefix reuse, and continuous batching, it treats KV-cache allocation as a first-class optimization lever, a necessary step for long-context, high-throughput serving. TGI built on that foundation but pulled the center of gravity toward enterprise needs: quantization, autoscaling, and observability at cluster scale. SGLang took another path, embedding a scripting layer to choreograph multi-step reasoning and multimodal flows, a move aligned with the rise of agentic workloads. At the hyperscale frontier, Dynamo pushed disaggregation splitting prefill and decode into separate execution pools and backed it with high-bandwidth interconnect libraries and dynamic routing. On the orchestration side, AIBrix and llm-d hardwired Kubernetes-native control, from policy enforcement and adapter management to pooled KV caches and inference gateways. The next generation is already surfacing. Triton and TensorRT-LLM bring compiler-first strategies, fusing kernels and optimizing graphs for maximum accelerator efficiency. DeepSpeed Inference bridges training and serving with ZeRO-style partitioning and kernel fusion, attractive for teams demanding one stack across the lifecycle. Meanwhile, vTensor and LightLLM strip things down to operator fusion, quantization, and developer ergonomics lean runtimes for agile experimentation. What it means for the stack: memory-aware kernels, disaggregated execution, and compiler-level optimization form the substrate. On top, orchestration planes enforce SLOs, scaling, and governance. At the edge, workflow programmability enables multi-model reasoning. The inference stack is no longer monolithic, it’s layered, modular, and specialized. The challenge for us is composing these layers into coherent deployments that maximize both performance and control. Note: In this post, I’ve covered established frameworks (vLLM, TGI, SGLang, Dynamo, AIBrix, llm-d) and the emerging wave (Triton, TensorRT-LLM, DeepSpeed Inference, vTensor, LightLLM). Together they illustrate how inference is evolving into a modular, multi-layered stack where memory, disaggregation, orchestration, and programmability define the next frontier. https://lnkd.in/eAVBAy7i

Once you put an LLM into production, two things start to dominate very quickly: cost and latency. Early on, prompt engineering works fine. But as usage grows, prompts get longer, outputs get verbose, and every request pays the price of a large general-purpose model. Latency creeps up. Bills do too. This is where customization starts to make sense. With Supervised Fine-Tuning (SFT), you teach the model your desired outputs directly—formats, tone, business rules. That alone lets you shrink prompts dramatically and produce shorter, more structured responses. With Reinforcement Fine-Tuning (RFT), you go further—optimizing behavior using verifiable programmatic rewards (Python checks, schema validation) or AI feedback (LLM-as-a-judge). The result is a model that does exactly what you need, without extra instructions. What you get in practice: • Lower latency — smaller tuned models encode shorter prompts faster and generate fewer tokens. It’s common to move from multi-second responses to sub-second latency. • Lower cost — shorter prompts + fewer output tokens + smaller models compound. At scale, this often translates to 5–10× lower inference cost for the same workload. • More predictable behavior — consistent structure, fewer retries, and less downstream cleanup. Customization isn’t about chasing model size. It’s about removing waste: wasted tokens, wasted instructions, wasted retries. If you’re running repeated workflows—classification, extraction, summarization, routing—customization usually pays for itself faster than you expect. For more advanced use cases, continued pretraining (CPT) lets you build a domain-specialized foundation model when you want broad reuse across many tasks. Amazon Nova supports SFT, RFT, and CPT with managed workflows—making it easier to build faster, cheaper, production-ready models. Learn more here: https://lnkd.in/gfbq4ykD

Beefree just cracked the code on one of product development's toughest challenges: Serving both scrappy startups and demanding enterprise clients with the same platform. Their challenge? Enterprise customers needed custom LLM integrations for their AI writing features, while smaller teams wanted plug-and-play solutions. Talk about conflicting requirements. Here’s what they did… They gave developers full control through callbacks and dialog interfaces, and when users feel in control, adoption skyrockets. They built TWO solutions: 1️⃣ Ready-to-use tools for teams, who needed speed 2️⃣ Fully customizable options for those who needed control They started small and iterated fast. Instead of building image generation, alt-text, AND translations all at once, they focused solely on AI text generation first. Ship the MVP, collect feedback, expand. They looped customers in early. Enterprise customers weren't just end users; they became co-developers. This saved months of building the wrong thing. They tested everything in a different sort of way… Someone who didn't build the feature had to implement it using only the documentation. Brutal, but brilliant. The result was wide adoption across both segments and a playbook they can apply to every future feature. The takeaway: You CAN serve different customer segments without fragmenting your product. It just requires intentional design and ruthless user empathy.

2026-𝐫𝐞𝐚𝐝𝐲 𝐋𝐋𝐌 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐟𝐢𝐱 𝐭𝐡𝐚𝐭 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐭 𝐛𝐫𝐞𝐚𝐤𝐬 𝐲𝐨𝐮𝐫 𝐛𝐮𝐝𝐠𝐞𝐭 (𝐚𝐧𝐝 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞). → 1. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 • Cut redundant instructions to reduce tokens. • Use structured formats like JSON. • Keep system messages minimal. → 2. 𝐌𝐨𝐝𝐞𝐥 𝐑𝐢𝐠𝐡𝐭-𝐒𝐢𝐳𝐢𝐧𝐠 • Use small/medium models for 70% of queries. • Cascade to larger models only when needed. • Track cost per request. → 3. 𝐑𝐀𝐆 𝐟𝐨𝐫 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 • Retrieve only relevant chunks. • Keep embeddings fresh and consistent. • Reduce hallucinations without scaling model size. → 4. 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐖𝐢𝐭𝐡 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 • Use small, high-quality datasets. • Validate with behavioral test cases. • Remove noisy or inconsistent samples. → 5. 𝐂𝐚𝐜𝐡𝐞 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐏𝐨𝐬𝐬𝐢𝐛𝐥𝐞 • Cache embeddings and frequent responses. • Reuse validated outputs to save compute. • Reduce latency in high-traffic loops. → 6. 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐋𝐞𝐯𝐞𝐥 𝐏𝐫𝐨𝐟𝐢𝐥𝐢𝐧𝐠 • Test on real traffic, not lab prompts. • Monitor latency and error spikes. • Track token patterns weekly. → 7. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 • Tune chunk sizes for clarity. • Use hybrid search when needed. • Improve ranking with metadata signals. → 8. 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐈𝐧𝐩𝐮𝐭 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 • Filter incomplete or low-quality queries. • Add guardrails before calling the model. • Standardize user prompts. → 9. 𝐑𝐞𝐝𝐮𝐜𝐞 𝐎𝐯𝐞𝐫-𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 • Control max tokens. • Enforce tight output formats. • Avoid unnecessary elaboration. → 10. 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 • Watch for accuracy drift. • Track model downtime. • Refresh data and prompts regularly. follow Devjyoti Seal for more insights

Excited to share our production guide for building RAG-based LLM applications where we bridge the gap between OSS and closed-source LLMs. - 💻 Develop a retrieval augmented generation (RAG) LLM app from scratch. - 🚀 Scale the major workloads (load, chunk, embed, index, serve, etc.) across multiple workers. - ✅ Evaluate different configurations of our application to optimize for both per-component (ex. retrieval_score) and overall performance (quality_score). - 🔀 Implement LLM hybrid routing approach to bridge the gap b/w OSS and closed-source LLMs. - 📦 Serve the application in a highly scalable and available manner. - 💥 Share the 1st order and 2nd order impacts LLM applications have had on our products and org. 🔗 Links: - Blog post (45 min. read): https://lnkd.in/g34a9Zwp - GitHub repo: https://lnkd.in/g3zHFD5z - Interactive notebook: https://lnkd.in/g8ghFWm9 Philipp Moritz and I had a blast developing and productionizing this with the Anyscale team and we're excited to share Part II soon (more details in the blog post).

A blueprint for designing production LLM systems: From Notebooks to production For example, we will fine-tune an LLM and do RAG on social media data, but it can easily be adapted to any data. We have 4 core components. We will follow the feature/training/inference (FTI) pipeline architecture. 𝟭. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 It is based on an ETL that: - crawls your data from blogs and socials - standardizes it - loads it to a NoSQL database (e.g., MongoDB) As: - we work with text data, which is naturally unstructured - no analytics required → a NoSQL database fits like a glove. 𝟮. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 It takes raw articles, posts and code data points from the data warehouse, processes them, and loads them into a logical feature store. Let's focus on the logical feature store. As with any RAG-based system, a vector database is one of the central pieces of the infrastructure. We directly use a vector database as a logical feature store. Unfortunately, the vector database doesn't offer the concept of a training dataset. To implement this, we will wrap the retrieved data into a versioned, tracked, and shareable MLOps artifact. To conclude: - the training pipeline will use the instruct datasets as artifacts (offline) - the inference pipeline will query the vector DB for RAG (online) 𝟯. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 It consumes instruct datasets from the feature store, fine-tunes an LLM with it, and stores the tuned LLM weights in a model registry. More concretely, when a new instruct dataset is available in the logical feature store, we will trigger the training pipeline, consume the artifact, and fine-tune the LLM. We run multiple experiments to find the best model and hyperparameters. We will use an experiment tracker to compare and select the best hyperparameters. After the experimentation phase, we store and reuse the best hyperparameters for continuous training (CT). The LLM candidate's testing pipeline is triggered for a detailed analysis. If it passes, the model is tagged as accepted and deployed to production. Our modular design lets us leverage an ML orchestrator to schedule and trigger the pipelines for CT. 𝟰. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 It is connected to the model registry and logical feature store. From the model registry, it loads a fine-tuned LLM, and from the logical feature store, it accesses the vector DB for RAG. It receives client requests as queries through a REST API. It uses the fine-tuned LLM and vector DB to do RAG to answer the queries. Everything is sent to a prompt monitoring system to analyze, debug, and understand the system. #artificialintelligence #machinelearning #mlops