Ensuring SOLID Principles in LLM Integration

Are your LLM integrations fragile and difficult to maintain with the rest of your applications? Raw text from LLMs becomes a maintenance nightmare when it needs to feed into other parts of your software. When LLM outputs need to update databases, call APIs, or populate UIs, treating them as raw text creates brittle systems. Without proper structure, even slight variations in the model's response format can break your application, leading to constant patching and reliability issues. Effective AI Engineering #4: Structure Your Outputs for Reliable Systems 👇 The Problem Many developers retrieve natural language responses and then write separate code to parse them using string manipulation, regex, or complex conditional logic: [Code example - see BEFORE example] Why this approach falls short: - Brittle Integrations: The slightest change in the LLM's output phrasing, formatting, or capitalization breaks your downstream parsing logic, leading to constant maintenance headaches. - Difficult Validation: Reliably checking if extracted data (like dates or emails) is valid requires complex validation logic *after* fragile parsing. - Hard to Extend: Adding features like Chain-of-Thought reasoning or extracting more nuanced data requires complicating both the prompt *and* the brittle parsing code. - Inconsistent Handling: Parsing and validation logic often gets scattered across different parts of your application. The Solution: Design for Structure Upfront A better approach is to define the *structure* you need using a schema (like a Pydantic model) and use tools that help the LLM conform to it. This makes the interaction predictable and robust. [Code example - see AFTER example] Why this approach works better: - Reliable Integration: Your application code interacts with predictable Python objects, not fragile text, allowing clean data access (e.g., `result.action_items[0].assignee_email`). - Built-in Validation: Data schemas with validation rules (like `EmailStr` or custom validators) ensure you're working with valid data immediately after parsing. - Easier Extensibility: Need Chain-of-Thought? Add a `reasoning` field to the model and adjust the prompt. Downstream code accessing structured data remains unchanged. - Parsing Flexibility: Frameworks like Mirascope abstract how the structure is enforced (tool calling, JSON mode), insulating your code from LLM implementation details. The Takeaway Stop treating LLMs like mere text generators when their output needs to integrate with code. Define structured outputs using schemas and leverage frameworks that help LLMs conform to those structures. This approach makes your AI integrations more robust, easier to validate, and simpler to extend as your application evolves.

As an MLOps platform, we started by helping organizations implement responsible AI governance for traditional machine learning models. With principles of transparency, accountability, and oversight, our Guardrails enabled smooth model development. However, governing large language models (LLMs) like ChatGPT requires a fundamentally different approach. LLMs aren't narrow systems designed for specific tasks - they can generate nuanced text on virtually any topic imaginable. This presents a whole new set of challenges for governance. Here are some key components for evolving AI governance frameworks to effectively oversee large language models (LLMs): 1️⃣ Usage-Focused Governance: Focus governance efforts on real-world LLM usage - the workflows, inputs and outputs - rather than just the technical architecture. Continuously assess risks posed by different use cases. 2️⃣ Dynamic Risk Assessment: Identify unique risks presented by LLMs such as bias amplification and develop flexible frameworks to proactively address emerging issues. 3️⃣ Customized Integrations: Invest in tailored solutions to integrate complex LLMs with existing systems in alignment with governance goals. 4️⃣ Advanced Monitoring: Utilize state-of-the-art tools to monitor LLMs in real-time across metrics like outputs, bias indicators, misuse prevention, and more. 5️⃣ Continuous Accuracy Tracking: Implement ongoing processes to detect subtle accuracy drifts or inconsistencies in LLM outputs before they escalate. 6️⃣ Agile Oversight: Adopt agile, iterative governance processes to manage frequent LLM updates and retraining in line with the rapid evolution of models. 7️⃣ Enhanced Transparency: Incorporate methodologies to audit LLMs, trace outputs back to training data/prompts and pinpoint root causes of issues to enhance accountability. In conclusion, while the rise of LLMs has disrupted traditional governance models, we at Katonic AI are working hard to understand the nuances of LLM-centric governance and aim to provide effective solutions to assist organizations in harnessing the power of LLMs responsibly and efficiently. #LLMGovernance #ResponsibleLLMs #LLMrisks #LLMethics #LLMpolicy #LLMregulation #LLMbias #LLMtransparency #LLMaccountability

Training a Large Language Model (LLM) involves more than just scaling up data and compute. It requires a disciplined approach across multiple layers of the ML lifecycle to ensure performance, efficiency, safety, and adaptability. This visual framework outlines eight critical pillars necessary for successful LLM training, each with a defined workflow to guide implementation: 𝟭. 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗖𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Use diverse, clean, and domain-relevant datasets. Deduplicate, normalize, filter low-quality samples, and tokenize effectively before formatting for training. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Design efficient preprocessing pipelines—tokenization consistency, padding, caching, and batch streaming to GPU must be optimized for scale. 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝘀𝗶𝗴𝗻: Select architectures based on task requirements. Configure embeddings, attention heads, and regularization, and then conduct mock tests to validate the architectural choices. 𝟰. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 and 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Ensure convergence using techniques such as FP16 precision, gradient clipping, batch size tuning, and adaptive learning rate scheduling. Loss monitoring and checkpointing are crucial for long-running processes. 𝟱. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗠𝗲𝗺𝗼𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Leverage distributed training, efficient attention mechanisms, and pipeline parallelism. Profile usage, compress checkpoints, and enable auto-resume for robustness. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Regularly evaluate using defined metrics and baseline comparisons. Test with few-shot prompts, review model outputs, and track performance metrics to prevent drift and overfitting. 𝟳. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀: Mitigate model risks by applying adversarial testing, output filtering, decoding constraints, and incorporating user feedback. Audit results to ensure responsible outputs. 🔸 𝟴. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Adapt models for specific domains using techniques like LoRA/PEFT and controlled learning rates. Monitor overfitting, evaluate continuously, and deploy with confidence. These principles form a unified blueprint for building robust, efficient, and production-ready LLMs—whether training from scratch or adapting pre-trained models.

LangChain recently published a helpful step-by-step guide on building AI agents. 🔗 How to Build an Agent –https://lnkd.in/dKKjw6Ju It covers key phases: 1. Defining realistic tasks 2. Documenting a standard operating procedure 3. Building an MVP with prompt engineering 4. Connect & Orchestrate 5. Test & Iterate 6. Deploy, Scale, and Refine While the structure is solid, one important dimension that’s often overlooked in agent design is: efficiency at scale. This is where Lean Agentic AI becomes critical—focusing on managing cost, carbon, and complexity from the very beginning. Let’s take a few examples from the blog and view them through a lean lens: 🔍 Task Definition ➡️ If the goal is to extract structured data from invoices, a lightweight OCR + regex or deterministic parser may outperform a full LLM agent in both speed and emissions. Lean principle: Use agents only when dynamic reasoning is truly required—avoid using LLMs for tasks better handled by existing rule-based or heuristic methods 📋 Operating Procedures ➡️ For a customer support agent, identify which inquiries require LLM reasoning (e.g., nuanced refund requests) and which can be resolved using static knowledge bases or templates. Lean principle: Separate deterministic steps from open-ended reasoning early to reduce unnecessary model calls. 🤖 Prompt MVP ➡️ For a lead qualification agent, use a smaller model to classify lead intent before escalating to a larger model for personalized messaging. Lean principle: Choose the best-fit model for each subtask. Optimize prompt structure and token length to reduce waste. 🔗 Tool & Data Integration ➡️ If your agent fetches the same documentation repeatedly, cache results or embed references instead of hitting APIs each time. Lean principle: Reduce external tool calls through caching, and design retry logic with strict limits and fallbacks to avoid silent loops. 🧪 Testing & Iteration ➡️ A multi-step agent performing web search, summarization, and response generation can silently grow in cost. Lean principle: Measure more than output accuracy—track retry count, token usage, latency, and API calls to uncover hidden inefficiencies. 🚀 Deployment ➡️ In a production agent, passing the entire conversation history or full documents into the model for every turn increases token usage and latency—often with diminishing returns. Lean principle: Use summarization, context distillation, or selective memory to trim inputs. Only pass what’s essential for the model to reason, respond, or act.. Lean Agentic AI is a design philosophy that brings sustainability, efficiency, and control to agent development—by treating cost, carbon, and complexity as first-class concerns. For more details, visit 👉 https://leanagenticai.com/ #AgenticAI #LeanAI #LangChain #SustainableAI #LLMOps #FinOpsAI #AIEngineering #ModelEfficiency #ToolCaching #CarbonAwareAI LangChain

Apparently today is the day this needs repeating, so: If the security of your LLM-powered app rests on the assumption "The LLM will never produce [whatever]" then either you, your users, or both are going to have a very bad day at some point. 1. Assume prompt injection will succeed: secure your app to be robust to LLM generated content, whether attacker-controlled or hallucinations. 2. If the LLM can see it, the attacker can leverage it: Anything the LLM has access to, the attacker can try to exploit or abuse, not just for exfiltration but for persistence, lateral movement, misinfo, etc. Process sensitive data and sensitive tool calls separately from untrusted data. 3. Least autonomy is the new least privilege: The more autonomy the LLM has to plan and act independently, the harder it is to enforce security boundaries. Use the least autonomy required to do the thing you need the agent to do. Keep security tradeoffs in mind as part of the cost-benefit analysis.

Most organizations suck at LLM risk management. But if you can avoid these 5 common mistakes, you will do better. Ignoring hallucinations. → LLMs may generate plausible but completely false information. Instead, do this ↳ Cross-check outputs with clinical guidelines. ↳ Only use LLMs for non-critical support. Neglecting bias audits. → Bias in model data can perpetuate health inequities. Instead, do this ↳ Regularly assess for demographic disparities in outcomes. ↳ Involve multidisciplinary teams in reviewing training data. Bypassing privacy obligations. → Entering patient data exposes it to possible unauthorized access. Instead, do this ↳ Don’t use personal identifiable information. ↳ Ask patients for consent before using their data with AI Overlooking explainability. → Black-box models reduce trust among clinicians and patients. Instead, do this ↳ Select models that provide clear, interpretable logic. ↳ Use explainable AI tools for critical decision points. Rushing into clinical integration. → Hasty AI deployments risk costly errors and lost trust. Instead, do this ↳ Pilot new workflows in limited, supervised settings first. ↳ Collect feedback regularly from frontline users. Every decision in healthcare can change a life, for better or worse. So avoid the beginner mistakes at all costs: Relying blindly on AI outputs for diagnosis. Feeding personal health data into unsecured applications. Ignoring underlying biases in LLM medical suggestions. Choosing “black-box” models over explainable solutions. Overlooking the complex privacy laws protecting EU patients. Instead, focus on: Demanding transparent, auditable AI decision-making. Integrating strict data privacy by design principles. Regularly auditing models for emerging and historical bias. Limiting LLM use to clinician-verified, low-risk cases. Choosing explainable AI that builds patient trust. Clinicians must remain in control, always. With LLM in the care setting, demands on safety, trust, and transparency are high. How do you balance innovation and risks?

If you want agents that actually ship, I’d start with these 12 principles of agentic AI system design and refuse to compromise on them: 1. Goal-first, outcome-driven ↳ Start from explicit, measurable goals and encode them in prompts, schemas, and metrics. ↳ Keep objectives legible (mission owner, SLAs, KPIs) so every action maps to a business outcome. 2. Single-responsibility agents ↳ Use many small, focused agents; each owns one capability or workflow slice. ↳ Easier debugging, specialised prompts/tools, and clean agent replacement. 3. Plan–act–reflect loop ↳ Make the loop explicit: perceive → plan → act → reflect → update. ↳ Allow plan revision when signals change instead of blind forward motion. 4. Tools as APIs, not hacks ↳ Treat tools (RAG, DB ops, APIs, human contact) as typed, structured interfaces. ↳ Version tool contracts so tools and models evolve independently. 5. Own your control flow ↳ Don’t bury orchestration inside prompts; use workflows or state machines. ↳ LLM decides next step; your code enforces invariants and recovery. 6. Stateless reducer, explicit state ↳ Keep LLM calls pure; push durable state into memory stores, DBs, or logs. ↳ This enables retries, scaling, auditing, and avoids context-window drift. 7. Memory as a first-class subsystem ↳ Separate short-term context, long-term knowledge, and interaction history. ↳ Define strict read/write rules so memory stays meaningful and precise. 8. Multi-agent orchestration patterns ↳ Choose a pattern (supervisor, adaptive network, custom orchestrator) and stick to it. ↳ Standardise delegation, negotiation, and result merging to prevent agent sprawl. 9. Observability and traceability ↳ Log prompts, plans, tool calls, errors, and outputs in structured formats. ↳ Support trace replay and diffing to identify loops, tool spam, failures. 10. Safety, guardrails, and human-in-the-loop ↳ Enforce auth, scoping, and policy at the orchestration layer—not just via prompts. ↳ Provide escalation paths for approvals or handoff when confidence drops. 11. Robustness through idempotence and recovery ↳ Make actions idempotent or compensatable so retries are safe. ↳ Use timeouts, backoff, circuit breakers, and degraded-operation strategies. 12. Continuous evaluation and improvement ↳ Track task-level and system-level metrics (success, latency, cost, overrides). ↳ Use synthetic tests, canaries, and log replays to evolve prompts and tools safely. Agentic AI isn’t “add more agents and hope something smart emerges.” It’s disciplined system design with a stochastic core. ♻️ 𝗥𝗲𝗽𝗼𝘀𝘁 to help more engineers move beyond prompt chains to real systems.

🚀 LLM Co-Scientists? One of the hallmarks of the MSA meeting is a surge of interest - both in academia and industry - towards embedding LLMs, reasoning, and agentic systems into instrument control for microscopy tools (and potentially labs combining microscopy and synthesis). At first glance, the value proposition is irresistible: LLMs can engage in iterative “dialogue” with scientists, generating code snippets on the fly and deploying them straight to the microscope. But there is the elephant in the room - today’s LLMs struggle with mathematical abstractions and rigorous reasoning. Meanwhile, for decades we’ve refined control, optimization, and simple ML-driven workflows on microscopes optimizing imaging parameters, autofocus routines, and particle counts via fixed policies and traditional optimization methods. While less glorious then LLMs, these ground-up developments from simple controls to e.g. algorithms that allow aberration correctors to function are the real building blocks on which progress to date has been based. How do we bring these two worlds together? I believe the answer lies in designing intermediate data representations that bridge free-form LLM reasoning with the constrained, formal workflows our instruments require. We need to translate human prompts into well-defined abstractions - things like causal direction indicators (see Aleksander Molak recent work), reward functions that quantify experimental goals, schema-based heuristics, or even domain-specific knowledge graphs. By introducing and exploring this semantic layer in a targeted fashion, we can: - Harness LLM flexibility without letting them wander into confabulation. - Maintain mathematical rigor by grounding prompts in formally defined objects. - Accelerate instrument integration by generating code that maps directly onto validated workflows. Without this intermediate data level, we risk relegating LLMs to isolated task-automation or chasing ill-fated experiments in the “LLM wilderness.” It will be abstractions and benchmarks that will make AI-driven microscopy both powerful and trustworthy. #Microscopy #AIinScience #LLM #WorkflowIntegration #DataModeling

🔍 Are LLMs making your authorization decisions? Think twice. (PS: An authorization decision is the system's way of confirming if a user has the correct permissions to access a resource, like a confidential report, ensuring sensitive data is only accessible to those with proper clearance.) AI is moving fast, and integrating large language models (LLMs) into your systems can unlock powerful capabilities—but it also introduces some unique security challenges. The Cloud Security Alliance just released its latest paper, Securing LLM Backed Systems: Essential Authorization Practices, and I think it’s a great read despite being only 34 pages long. Props to the lead authors Nate Lee and Laura Cristiana Voicu. My takeaway? The classic CIA security triad still applies: confidentiality, integrity, and availability. Three things stood out to me: 1) Keep Authorization Outside the LLM: Never rely on LLMs to make authorization decisions. For example, ensure that access to sensitive customer data (Confidentiality) is controlled by a separate, deterministic system rather than the LLM itself. 2) Validate Everything: Input and output validation is essential to protect against prompt injection attacks and data leaks. For instance, validate all incoming data to prevent malicious content from influencing the LLM's outputs (Integrity). 3) Secure the Orchestrator: Your orchestrator must handle identity and authorization checks before data reaches the LLM. For example, before the LLM processes a request, the orchestrator should verify the user's identity and permissions to ensure they can access the requested information (Availability). Even as we innovate with AI, the classic CIA triad—Confidentiality, Integrity, Availability—remains a cornerstone of good security. Build security into your AI systems from the ground up. 💡 #CyberSecurity #AI #LLM

How do we make sure LLMs stay safe, fair, and reliable in real-world use? Here’s a breakdown of the different types of guardrails that guide LLMs in the right direction,  from adapting in real time to filtering inappropriate content and ensuring legal compliance. Here’s what each type focuses on: 1. Adaptive Guardrails Helps models adjust behavior in real time and stay compliant with changing contexts. 2. Compliance Guardrails Ensures outputs follow laws and data protection regulations in specific sectors. 3. Contextual Guardrails Fine-tunes model behavior for specific use cases and avoids misleading responses. 4. Ethical Guardrails Keeps outputs within socially accepted norms and avoids harmful or biased content. 5. Security Guardrails Protects against both internal and external threats, like data breaches or misuse. 6. Input & Output Guardrails Validates prompts and responses to ensure safety, accuracy, and relevance. Save this if you're working with LLMs and want to understand how responsible AI is being built.