Implementation Of Frameworks

If you’re building anything with LLMs, your system architecture matters more than your prompts. Most people stop at “call the model, get the output.” But LLM-native systems need workflows, blueprints that define how multiple LLM calls interact, how routing, evaluation, memory, tools, or chaining come into play. Here’s a breakdown of 6 core LLM workflows I see in production: 🧠 LLM Augmentation Classic RAG + tools setup. The model augments its own capabilities using: → Retrieval (e.g., from vector DBs) → Tool use (e.g., calculators, APIs) → Memory (short-term or long-term context) 🔗 Prompt Chaining Workflow Sequential reasoning across steps. Each output is validated (pass/fail) → passed to the next model. Great for multi-stage tasks like reasoning, summarizing, translating, and evaluating. 🛣 LLM Routing Workflow Input routed to different models (or prompts) based on the type of task. Example: classification → Q&A → summarization all handled by different call paths. 📊 LLM Parallelization Workflow (Aggregator) Run multiple models/tasks in parallel → aggregate the outputs. Useful for ensembling or sourcing multiple perspectives. 🎼 LLM Parallelization Workflow (Synthesizer) A more orchestrated version with a control layer. Think: multi-agent systems with a conductor + synthesizer to harmonize responses. 🧪 Evaluator–Optimizer Workflow The most underrated architecture. One LLM generates. Another evaluates (pass/fail + feedback). This loop continues until quality thresholds are met. If you’re an AI engineer, don’t just build for single-shot inference. Design workflows that scale, self-correct, and adapt. 📌 Save this visual for your next project architecture review. 〰️〰️〰️ 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

𝗟𝗟𝗠 -> 𝗥𝗔𝗚 -> 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 -> 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 The visual guide explains how these four layers relate—not as competing technologies, but as an evolving intelligence architecture. Here’s a deeper look: 1. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) This is the foundation. Models like GPT, Claude, and Gemini are trained on vast corpora of text to perform a wide array of tasks: – Text generation – Instruction following – Chain-of-thought reasoning – Few-shot/zero-shot learning – Embedding and token generation However, LLMs are inherently limited to the knowledge encoded during training and struggle with grounding, real-time updates, or long-term memory. 2. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) RAG bridges the gap between static model knowledge and dynamic external information. By integrating techniques such as: – Vector search – Embedding-based similarity scoring – Document chunking – Hybrid retrieval (dense + sparse) – Source attribution – Context injection …RAG enhances the quality and factuality of responses. It enables models to “recall” information they were never trained on, and grounds answers in external sources—critical for enterprise-grade applications. 3. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 RAG is still a passive architecture—it retrieves and generates. AI Agents go a step further: they act. Agents perform tasks, execute code, call APIs, manage state, and iterate via feedback loops. They introduce key capabilities such as: – Planning and task decomposition – Execution pipelines – Long- and short-term memory integration – File access and API interaction – Use of frameworks like ReAct, LangChain Agents, AutoGen, and CrewAI This is where LLMs become active participants in workflows rather than just passive responders. 4. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 This is the most advanced layer—where we go beyond a single autonomous agent to multi-agent systems with role-specific behavior, memory sharing, and inter-agent communication. Core concepts include: – Multi-agent collaboration and task delegation – Modular role assignment and hierarchy – Goal-directed planning and lifecycle management – Protocols like MCP (Anthropic’s Model Context Protocol) and A2A (Google’s Agent-to-Agent) – Long-term memory synchronization and feedback-based evolution Agentic AI is what enables truly autonomous, adaptive, and collaborative intelligence across distributed systems. Whether you’re building enterprise copilots, AI-powered ETL systems, or autonomous task orchestration tools, knowing what each layer offers—and where it falls short—will determine whether your AI system scales or breaks. If you found this helpful, share it with your team or network. If there’s something important you think I missed, feel free to comment or message me—I’d be happy to include it in the next iteration.

𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗰𝘆𝗰𝗹𝗲 and definitely not an occasional burst of inspiration or isolated ideas. It’s a continuous, structured process that drives sustainable business results and operational improvements. It transforms internal requirements and external market insights into enhanced outcomes through collaboration, experimentation and adaptation. As a framework it creates an infinite cycle focusing on: 1️⃣ 𝗡𝗲𝗲𝗱𝘀 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 & 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 to fully understand potential areas for innovation stakeholders care about and who could be aligned with market trends and opportunities. 🔹 Innovation needs a clear purpose and pain points or opportunities to solve. 2️⃣ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗖𝗼-𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻 & 𝗜𝗱𝗲𝗮𝘁𝗶𝗼𝗻 by engaging suppliers, startups, and cross-functional teams to propose solutions and develop prototypes. 🔹 Innovation is a result of teaming up across disciplines and partnering. 3️⃣ 𝗣𝗶𝗹𝗼𝘁 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 to test and continuously refine solutions in a controlled environment based on product feedback and market changes. 🔹 Innovation lives from experimenting, understanding and fine-tuning. 4️⃣ 𝗦𝗰𝗮𝗹𝗲 & 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗶𝗺𝗽𝗮𝗰𝘁 that leads to the desired results such as cost efficiencies, improved workflows or enhanced supplier performance. 🔹 Innovation creates value when results can be scaled and demonstrated. 5️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 & 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 results and gather lessons learnt to inform further performance improvement or future innovation cycles. 🔹 Innovation must be measurable, both metrics and learning wise. 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲. One that empowers Procurement teams to move beyond transactions and enables enterprises to harness a wealth of possible outcomes resulting from a collaboration of internal experts and an ecosystem of external partners. ❓How does your organisation approach Procurement innovation, if at all? ❓Are traditional mindsets, as many commented on an earlier post, and a risk-averse culture the biggest barriers to innovate? Looking forward to read your views in the comments.

Why Doesn’t New Zealand Have a Simple Framework for Product Management in SMEs? A student in my Product Management course recently pointed out a one-day workshop in Qatar run by the Qatar Research Development and Innovation Council. It teaches SMEs a simple product management framework—Strategy, Innovation, Detail, Launch, and Management (SIDLM). As far as I can tell, SIDLM is unique to the program, though the elements appear in many models of the product management process. He asked: 💡 Why don’t we have something like this in New Zealand? Easy to remember, easy to teach. Good question. Let’s create one! While I’m teaching product management this quarter, let’s co-develop a framework tailored for New Zealand’s SMEs—one that is: ✅ Simple to remember and apply – because SMEs don’t have time for complexity. ✅ Generalizable across sectors – whether you’re in agri-tech, SaaS, or manufacturing. ✅ Practical and inexpensive to deploy – so businesses can use it from day one (and it doesn't need consultants to help understand it 😊). ✅ Aligned with NZ’s unique business landscape – small, nimble firms with global ambition. Introducing BUILD: A Starting Point I’ve taken inspiration from existing models but simplified them into the acronym "BUILD": 🔹 B – Business & Market Fit – Start by validating that your product meets real market needs. 🔹 U – User-Centred Innovation on a Budget – Innovate efficiently with limited resources. 🔹 I – Implementation & Agile Execution – Move fast and adapt as you learn. 🔹 L – Lean Launch & Market Entry – Test, iterate, and refine before scaling. 🔹 D – Data-Driven Scaling & Continuous Improvement – Use data to fuel sustainable growth. This is just a starting point—I look forward to your feedback! Over the next 10 weeks, I’ll post about each step in the BUILD framework, sharing examples and inviting input from my students and the LinkedIn community. What do you think? What’s missing? What would make this more useful for SMEs in New Zealand? Let’s co-create something valuable. #ProductManagement #SMEs #Innovation #Entrepreneurship #BUILDFramework #NewZealandBusiness #ProductLeadership #BusinessGrowth

🔍 Another massive analysis of 457 LLMOps case studies - and wow, this is the real-world implementation data we've been missing. After sifting through 600,000+ words of technical documentation, we've distilled the actual engineering patterns that work in production. Not theoretical architectures or proof-of-concepts, but battle-tested implementations across enterprises, startups, and everything in between. Key insights that jumped out: - RAG isn't just about throwing vectors in a database - companies like Doordash achieved 90% hallucination reduction through careful quality control - Fine-tuning smaller models often outperforms larger ones in production (with receipts from multiple companies showing 5-10x cost reductions) - The shift from basic prompting to sophisticated orchestration isn't just hype - it's driving real metrics What makes this particularly valuable: Each case study breaks down the nitty-gritty technical decisions teams made, from model selection to infrastructure choices. It's essentially a massive knowledge transfer from teams who've already solved these problems. Deep dive here: https://lnkd.in/dRv-cs5J Seriously worth a read if you're implementing LLMs in production or planning to. The summaries alone are worth their weight in GPU hours 🚀 #LLMOps #MLEngineering #ProductionAI #GenerativeAI #TechArchitecture P.S. Would love to hear from others who've tackled similar challenges - what patterns have you found most effective in production?

🚨 Mastering IT Risk Assessment: A Strategic Framework for Information Security In cybersecurity, guesswork is not strategy. Effective risk management begins with a structured, evidence-based risk assessment process that connects technical threats to business impact. This framework — adapted from leading standards such as NIST SP 800-30 and ISO/IEC 27005 — breaks down how to transform raw threat data into actionable risk intelligence: 1️⃣ System Characterization – Establish clear system boundaries. Define the hardware, software, data, interfaces, people, and mission-critical functions within scope. 🔹 Output: System boundaries, criticality, and sensitivity profile. 2️⃣ Threat Identification – Identify credible threat sources — from external adversaries to insider risks and environmental hazards. 🔹 Output: Comprehensive threat statement. 3️⃣ Vulnerability Identification – Pinpoint systemic weaknesses that can be exploited by these threats. 🔹 Output: Catalog of potential vulnerabilities. 4️⃣ Control Analysis – Evaluate the design and operational effectiveness of current and planned controls. 🔹 Output: Control inventory with performance assessment. 5️⃣ Likelihood Determination – Assess the probability that a given threat will exploit a specific vulnerability, considering existing mitigations. 🔹 Output: Likelihood rating. 6️⃣ Impact Analysis – Quantify potential losses in terms of confidentiality, integrity, and availability of information assets. 🔹 Output: Impact rating. 7️⃣ Risk Determination – Integrate likelihood and impact to determine inherent and residual risk levels. 🔹 Output: Ranked risk register. 8️⃣ Control Recommendations – Prioritize security enhancements to reduce risk to acceptable levels. 🔹 Output: Targeted control recommendations. 9️⃣ Results Documentation – Compile the process, findings, and mitigation actions in a formal risk assessment report for governance and audit traceability. 🔹 Output: Comprehensive risk assessment report. When executed properly, this process transforms IT threat data into strategic business intelligence, enabling leaders to make informed, risk-based decisions that safeguard the organization’s assets and reputation. 👉 Bottom line: An organization’s resilience isn’t built on tools — it’s built on a disciplined, repeatable approach to understanding and managing risk. #CyberSecurity #RiskManagement #GRC #InformationSecurity #ISO27001 #NIST #Infosec #RiskAssessment #Governance

Stop obsessing over which LLM is better. It does not matter if your architecture is weak. A junior dev optimizes prompts. A senior dev optimizes flow control. If you want to move from "demo" to "production", you need to master these 4 agentic patterns: 𝟭. 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 (𝗖𝗼𝗧) This is your debugging layer for logic. Standard models fail at complex math or reasoning because they predict the answer token immediately. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Do not just ask for the result. In your System Prompt, explicitly instruct the model to "think step-by-step" or output its reasoning inside specific XML tags (e.g., <reasoning>...</reasoning>) before the final answer. You can parse and validate the reasoning steps programmatically before showing the final result to the user. 𝟮. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) This is your dynamic context injection. The context window is finite; your data is not. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 ◼️ Ingest: Chunk your documents and store them as vector embeddings (using Pinecone, Milvus, or pgvector). ◼️ Retrieve: On user query, perform a cosine similarity search to find the top-k chunks. ◼️ Inject: Concatenate these chunks into the context string of your prompt before sending the request to the LLM. 𝟯. 𝗥𝗲𝗔𝗰𝘁 (𝗥𝗲𝗮𝘀𝗼𝗻 + 𝗔𝗰𝘁 𝗟𝗼𝗼𝗽) This is how you break out of the text box. It turns the LLM into a controller for your own functions. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 You need a while loop in your code: 1. Call the LLM with a list of defined tools (JSON Schema). 2. Check if the finish_reason is tool_calls. 3. Execute: Run the requested function locally (e.g., fetch_weather(city)). 4. Observe: Append the function's return value to the message history. 5. Loop: Send the history back to the LLM to generate the final natural language response. 𝟰. 𝗥𝗼𝘂𝘁𝗲𝗿 (𝗧𝗵𝗲 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿) This is your switch statement powered by semantic understanding. Using a massive model for every trivial task is inefficient and slow. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Use a lightweight, fast model (like GPT-4o-mini or a local Llama 3 8B) as the entry point. Its only job is to classify the user intent into a category ("Coding", "General Chat", "Database Query"). Based on this classification, your code routes the request to the appropriate specialized prompt or agent. - - - - - - - - - - - - - - - 𖤂 Save this post, you’ll want to revisit it. - - - - - - - - - - - - - - - - I’m Nina. I build with AI and share how it’s done weekly. #aiagents #llm #softwaredevelopment #technology

The Future of AI is Agent Architecture   𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 An agent architecture is a framework that enables AI systems to think and operate more like humans do - with memory, planning capabilities, and the ability to use tools effectively to solve real-world complex problems.   This architecture creates systems that don't just follow instructions but actually reason through challenges and learn from their experiences.   𝗖𝗼𝗿𝗲 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 1. Memory: Divided into short-term (for immediate context) and long-term (for persistent knowledge), allowing the agent to maintain continuity across interactions.   2. Tools: A suite of capabilities including VectorSearch(), TextGeneration(), CodeExecutor(), WebBrowser(), ImageAnalysis(), KnowledgeGraph(), DocAnalysis(), and RAGRetrieval().   3. Planning: The cognitive center featuring reflection, self-criticism, chain of thoughts, and subgoal decomposition for sophisticated problem-solving.   4. Action: The execution layer that implements plans and processes feedback to improve future performance.   𝗞𝗲𝘆 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 1. Self-Correction: The architecture enables agents to critique their own work before execution, significantly reducing errors and resource waste.   2. Human-Like Reasoning: By mimicking human cognitive processes, these agents can solve problems with greater nuance.   3. Continuous Improvement: The feedback loop between action and planning creates a system that learns from every interaction.   While most organizations are still focused on implementing basic RAG systems, this agent architecture represents the next phase in AI capabilities.   I believe this approach will fundamentally change how we build AI systems, moving from simple task executors to true reasoning partners.   Over to you: What do you think?

🔐 Understanding the 7 Steps of the NIST Risk Management Framework (RMF) If you're working in risk, compliance, IT security, or vendor oversight, you've likely heard of the NIST RMF. But what does implementation look like? Here's a breakdown of the 7 steps: 1. Prepare – Laying the Foundation for Risk-Informed Decisions What it means: Establish a strong starting point by identifying key stakeholders, roles, and responsibilities. Clarify who’s accountable for what across cybersecurity, privacy, procurement, compliance, and risk. In practice: Define the roles of system owners, authorizing officials, control assessors, etc. - Create an inventory of all information systems. - Understand the organization's risk tolerance and priorities. 2. Categorize – Understanding the Business Impact of Your Systems What it means: Classify each system based on how critical it is and what kind of data it processes. This step drives the rigor of the controls you’ll need to apply. In practice: Use FIPS 199 and NIST SP 800-60 to assign impact levels (low, moderate, high) for confidentiality, integrity, and availability. - Engage with business owners to understand how downtime or data compromise would affect operations. 3. Select – Choosing the Right Security Controls What it means: Now that the system is categorized, select appropriate security and privacy controls from NIST SP 800-53, based on the impact level. In practice: Use control baselines (Low/Moderate/High) as a starting point. - Tailor controls by adding compensating controls or removing those not applicable. 4. Implement – Bringing Controls to Life What it means: Deploy the selected controls and document how they work in your environment. This step bridges policy and practice. In practice: Configure systems based on secure baseline settings. - Train personnel on relevant control processes (e.g., incident response). 🔍 5. Assess – Testing What You Built What it means: Verify that controls are implemented correctly and doing what they’re supposed to do. In practice: Conduct control assessments (e.g., technical testing, documentation review, interviews). - Use independent assessors where required. 6. Authorize – Making a Risk-Based Decision What it means: Senior officials decide whether to authorize the system to operate, based on the residual risk identified during assessment. In practice: Prepare a risk summary (including known weaknesses and POAMs – Plans of Action and Milestones). - Articulate business benefits vs. residual risk. 7. Monitor – Stay Sharp, Stay Safe What it means: Continuously monitor system controls and risk posture. The environment, threats, and vendors are constantly changing. In practice: Conduct periodic control reviews and vulnerability scans. - Track changes in system architecture or third-party integrations. #NISTRMF #CyberSecurity #TPRM #InformationRisk #ThirdPartyRisk #Governance #Compliance #RiskManagement #SecurityFramework #3prm Source: https://grclab.com