Using LLMs for Routine and Complex Business Logic

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

I recently spent time getting more hands-on with LLM & Agentic AI engineering through Ed Donner's training. Instead of stopping at examples, I built a mini multi-agent logistics delivery optimization framework. Building real AI systems quickly makes one thing clear: 𝙏𝙝𝙚 𝙝𝙖𝙧𝙙 𝙥𝙖𝙧𝙩 𝙞𝙨𝙣’𝙩 𝙩𝙝𝙚 𝙢𝙤𝙙𝙚𝙡 — 𝙞𝙩’𝙨 𝙩𝙝𝙚 𝙖𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝙙𝙚𝙘𝙞𝙨𝙞𝙤𝙣𝙨 𝙖𝙧𝙤𝙪𝙣𝙙 𝙞𝙩. A few practical lessons: 1. 𝗟𝗟𝗠 𝗺𝗼𝗱𝗲𝗹 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗳𝗮𝗿 𝗺𝗼𝗿𝗲 𝗻𝘂𝗮𝗻𝗰𝗲𝗱 𝘁𝗵𝗮𝗻 𝗰𝗼𝘀𝘁 𝘃𝘀 𝗹𝗮𝘁𝗲𝗻𝗰𝘆. Trade-offs: • reasoning maturity for complex planning • context window & memory strategy • proprietary models vs smaller open models • infra costs (GPU/hosting) vs token-based API costs • tool-calling reliability & structured output adherence • benchmark performance vs real task behavior • model stability across releases In practice, it becomes a hybrid strategy: 𝘀𝗺𝗮𝗹𝗹𝗲𝗿/𝗰𝗵𝗲𝗮𝗽𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗿𝗼𝘂𝘁𝗶𝗻𝗲 𝘁𝗮𝘀𝗸𝘀 + 𝗦𝗟𝗠 𝘄𝗶𝘁𝗵 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗱𝗼𝗺𝗮𝗶𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 + 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. 𝟮. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗮𝘀 𝗺𝘂𝗰𝗵 𝗮𝘀 𝘁𝗵𝗲 𝗟𝗟𝗠: Many AI demos over-engineer the stack. In reality, simplicity, latency, security and reliability matter more than novelty. • Use orchestration frameworks only where coordination complexity exists • Combine prompts with structured outputs to reduce ambiguity • Watch serialization and tool-call overhead — they impact latency and UX • Reduce unnecessary LLM calls when deterministic code can solve the task Besides lowering token cost, this improves context efficiency, letting models focus on real reasoning. Sometimes best architecture decision is 𝙣𝙤𝙩 𝙞𝙣𝙩𝙧𝙤𝙙𝙪𝙘𝙞𝙣𝙜 𝙖𝙣𝙤𝙩𝙝𝙚𝙧 𝙡𝙖𝙮𝙚𝙧. 3. 𝗕𝗶𝗴𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 ≠ 𝗯𝗲𝘁𝘁𝗲𝗿 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 Smaller models with fine-tuning on domain data can perform more consistently than larger ones. Fine-tuning helps when: • tasks are repetitive but require precision • domain vocabulary is specialized • prompts become fragile But 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗮𝗹𝘀𝗼 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲𝘀 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗼𝘃𝗲𝗿𝗵𝗲𝗮𝗱. Base model upgrades trigger retesting and partial rewrites. 4. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗴𝗮𝗽: 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 → 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 Demos are easy. Production requires 𝙚𝙫𝙖𝙡𝙪𝙖𝙩𝙞𝙤𝙣 𝙛𝙧𝙖𝙢𝙚𝙬𝙤𝙧𝙠𝙨, 𝙤𝙗𝙨𝙚𝙧𝙫𝙖𝙗𝙞𝙡𝙞𝙩𝙮, 𝙨𝙚𝙘𝙪𝙧𝙞𝙩𝙮, 𝙥𝙚𝙧𝙛𝙤𝙧𝙢𝙖𝙣𝙘𝙚, 𝙘𝙤𝙨𝙩 𝙜𝙤𝙫𝙚𝙧𝙣𝙖𝙣𝙘𝙚 & 𝙜𝙪𝙖𝙧𝙙𝙧𝙖𝙞𝙡𝙨. That’s where most engineering effort goes. 𝟱. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗔𝗜 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀 Many AI conversations focus on SDLC productivity- Useful but the bigger opportunity is 𝙧𝙚𝙞𝙢𝙖𝙜𝙞𝙣𝙞𝙣𝙜 𝙡𝙚𝙜𝙖𝙘𝙮 𝙗𝙪𝙨 𝙥𝙧𝙤𝙘𝙚𝙨𝙨𝙚𝙨 𝙪𝙨𝙞𝙣𝙜 𝘼𝙜𝙚𝙣𝙩𝙞𝙘 AI. By simply automating existing steps, we risk making inefficient tasks efficient and missing the real transformation.

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.

𝗧𝗼𝗽 𝟵 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 Most people think AI = prompt → response. But real AI systems are built using workflows, not just single prompts. These workflows define how LLMs: • break problems • reason step-by-step • use tools • collaborate • improve outputs Understanding these is key to building real AI agents. Here is a simple breakdown. 1. Prompt Chaining Break a task into multiple steps where each LLM call builds on the previous one. Used for: • chatbots • multi-step reasoning • structured workflows 2. Parallelization Run multiple LLM calls at the same time and combine results. Used for: • faster processing • evaluations • handling multiple inputs 3. Orchestrator–Worker A central LLM splits tasks and assigns them to smaller worker models. Used for: • agentic RAG • coding agents • complex task delegation 4. Evaluator–Optimizer One model generates output, another evaluates and improves it in a loop. Used for: • data validation • improving response quality • feedback-based systems 5. Router Classifies input and sends it to the right workflow or model. Used for: • customer support systems • multi-agent setups • intelligent routing 6. Autonomous Workflow The agent interacts with tools and environment, learns from feedback, and continues execution. Used for: • autonomous agents • real-world task execution 7. Reflexion The model reviews its own output and improves it iteratively. Used for: • complex reasoning • debugging tasks • self-correcting systems 8. ReWOO Separates planning and execution. One part plans tasks, others execute them. Used for: • deep research • multi-step problem solving 9. Plan and Execute The agent creates a plan, executes steps, and updates based on results. Used for: • business workflows • automation pipelines 💡 Simple mental model • Chaining → step-by-step thinking • Parallel → faster execution • Orchestrator → task distribution • Evaluator → quality improvement • Router → smart decision-making • Autonomous → self-running systems 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 Moving from: single prompts → structured workflows is what turns: LLMs → real AI systems Most people are still at the prompt level. The real power comes from designing workflows. Which workflow are you using the most right now? Image credits: Rakesh Gohel #AI #AIAgents #LLM #AgenticAI #GenAI #AIEngineering #Automation

💡 Why are we sending everything to expensive LLMs? The smarter architecture pattern I’m seeing work in the real world: ➡️ Route 70–90% of tasks to SLMs (Small Language Models) ➡️ Escalate only complex, ambiguous work to LLMs This isn’t just optimization—it’s necessary for scale. 🧠 Hybrid SLM + LLM Architecture (What Works) At the center is an AI Gateway that acts as the decision engine: - Classifies request complexity - Routes to the right model tier - Applies guardrails (PII, compliance) - Tracks cost + performance Execution model: - 🟢 SLMs → fast, cheap, high-volume tasks - 🔵 LLMs → deep reasoning, edge cases - 🔁 Fallback → escalate when confidence is low ⚡ Proven Patterns ✔️ SLM-first strategy (default routing) ✔️ Confidence-based escalation ✔️ Task decomposition (SLM → LLM chain) ✔️ RAG before generation ✔️ Aggressive caching ⚠️ Pitfalls I keep seeing ❌ Sending everything to LLMs → 💸 cost explosion ❌ Over-orchestrating “agentic” workflows → unnecessary complexity ❌ Ignoring latency → poor UX ❌ No cost observability → no control ❌ Same prompts for SLMs and LLMs → bad results 🧭 Simple mental model SLM = Worker LLM = Expert Let the workers handle the bulk. Call the expert only when it truly matters. 📊 What good looks like - 60–90% cost reduction - 2–5x faster response times - Better scalability without overengineering Most teams start with LLM-heavy designs. The winning approach is the opposite: 👉 Start small. Escalate to large. #AI #EnterpriseArchitecture #GenAI #AIArchitecture #CostOptimization #DigitalTransformation

Over the past few weeks, I validated several patterns that reveal how AI agents truly behave in production. Autonomy is impressive, but structure still delivers the most consistent results. In a traditional LLM workflow where logic and reasoning are fully orchestrated, the same model ran twice as fast and used twelve times fewer tokens than an agentic setup. Efficiency scales best when reasoning is guided, not left open-ended. When deterministic logic was moved into the orchestration layer, the agent gained flexibility, but it came at a cost: more time and higher token usage. Predictable performance, yet less efficient overall. The biggest insight came from reasoning models themselves. GPT 5, with its superior compression and contextual efficiency, outperformed GPT 4o not because it was larger, but because it reasoned more precisely. What my findings validated: For simple and well-defined use cases, LLM workflows can achieve over 99% reliability without complex agent logic. A verifier layer - a lightweight “check my work” agent, can further improve reliability and confidence. For complex, critical, or regulated processes, orchestration remains faster, cheaper, and more auditable. Autonomy sounds exciting, but it isn’t always the optimal path. The smartest systems know when to act independently and when to rely on structured reasoning. AI agents perform best within boundaries that balance adaptability with control. Use them where discovery and contextual reasoning create value. Rely on orchestration where precision, governance, and cost efficiency are non-negotiable.

I've put my last 6 months building and selling AI Agents I've finally have "What to Use Framework" LLMs → You need fast, simple text generation or basic Q&A → Content doesn't require real-time or specialized data → Budget and complexity need to stay minimal → Use case: Customer FAQs, email templates, basic content creation RAG: → You need accurate answers from your company's knowledge base → Information changes frequently and must stay current → Domain expertise is critical but scope is well-defined → Use case: Employee handbooks, product documentation, compliance queries AI Agents → Tasks require multiple steps and decision-making → You need integration with existing tools and databases → Workflows involve reasoning, planning, and memory → Use case: Sales pipeline management, IT support tickets, data analysis Agentic AI → Multiple specialized functions must work together → Scale demands coordination across different systems → Real-time collaboration between AI capabilities is essential → Use case: Supply chain optimization, smart factory operations, financial trading My Take: Most companies jump straight to complex agentic systems when a simple RAG setup would solve 80% of their problems. Start simple, prove value, then scale complexity. Take a Crawl, Walk, Run approach with AI I've seen more AI projects fail from over-engineering than under-engineering. Match your architecture to your actual business complexity, not your ambitions. P.S. If you're looking for right solutions, DM me - I answer all valid DMs 👋 .

I’ve been building and managing data systems at Amazon for the last 8 years. Now that AI is everywhere, the way we work as data engineers is changing fast. Here are 5 real ways I (and many in the industry) use LLMs to work smarter every day as a Senior Data Engineer: 1. Code Review and Refactoring LLMs help break down complex pull requests into simple summaries, making it easier to review changes across big codebases. They can also identify anti-patterns in PySpark, SQL, and Airflow code, helping you catch bugs or risky logic before it lands in prod. If you’re refactoring old code, LLMs can point out where your abstractions are weak or naming is inconsistent, so your codebase stays cleaner as it grows. 2. Debugging Data Pipelines When Spark jobs fail or SQL breaks in production, LLMs help translate ugly error logs into plain English. They can suggest troubleshooting steps or highlight what part of the pipeline to inspect next, helping you zero in on root causes faster. If you’re stuck on a recurring error, LLMs can propose code-level changes or optimizations you might have missed. 3. Documentation and Knowledge Sharing Turning notebooks, scripts, or undocumented DAGs into clear internal docs is much easier with LLMs. They can help structure your explanations, highlight the “why” behind key design choices, and make onboarding or handover notes quick to produce. Keeping platform wikis and technical documentation up to date becomes much less of a chore. 4. Data Modeling and Architecture Decisions When you’re designing schemas, deciding on partitioning, or picking between technologies (like Delta, Iceberg, or Hudi), LLMs can offer quick pros/cons, highlight trade-offs, and provide code samples. If you need to visualize a pipeline or architecture, LLMs can help you draft Mermaid or PlantUML diagrams for clearer communication with stakeholders. 5. Cross-Team Communication When collaborating with PMs, analytics, or infra teams, LLMs help you draft clear, focused updates, whether it’s a Slack message, an email, or a JIRA comment. They’re useful for summarizing complex issues, outlining next steps, or translating technical decisions into language that business partners understand. LLMs won’t replace data engineers, but they’re rapidly raising the bar for what you can deliver each week. Start by picking one recurring pain point in your workflow, then see how an LLM can speed it up. This is the new table stakes for staying sharp as a data engineer.

Please stop building multi-agent systems. Autonomy means nothing if the system can’t repeat its own success 𝟭. 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗻𝗲𝗲𝗱𝘀 "𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴" It isn’t about “can it solve the problem?” It’s “can it solve the problem under real constraints, and still make business sense?” ↳ Constraints: cost, latency, accuracy, compliance, security, privacy, ethics ↳ Value: measurable user impact (time saved, risk reduced, revenue unlocked) ↳ Unit economics: margins today or a credible path soon Add even one constraint, and the search space explodes. Add scale, and it gets harder again. 𝟮. 𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝘁 𝗮𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲, 𝘀𝗵𝗮𝗸𝘆 𝗮𝘁 𝗮𝗱𝗵𝗲𝗿𝗲𝗻𝗰𝗲 The creative variability we love trades off with reliability. ↳ Non-deterministic outputs ↳ Instruction drift across long tasks ↳ Sensitivity to prompt/context formatting Great for ideation and synthesis; fragile for strict, long-horizon execution. 𝟯. 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝗺𝗲𝗮𝗻𝘀 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 To tame non-determinism, you have to add structure. A lot of it. ↳ Task decomposition and state: break work into verifiable steps, persist state ↳ Data layer: sourcing → cleaning → chunking → embeddings → indexing (RAG) ↳ Prompt lifecycle: versioning, testing, registries, rollout/rollback ↳ Model routing & caching: pick the smallest model that meets quality, reuse context ↳ Evals & observability: ground-truth tests, regression suites, traces, guardrails ↳ The triangle you must balance every day: accuracy ↔ cost ↔ latency Yes, the “mammoth thinking model” can brute-force quality, only if your users can wait and you can eat the bill. Most can’t. 𝟰. 𝗧𝗿𝗲𝗮𝘁 𝗔𝗜 𝗮𝘀 𝗮 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗶𝗻 𝗮 𝘀𝘆𝘀𝘁𝗲𝗺, 𝘁𝗵𝗲𝗻 𝗰𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝘀𝗶𝗺𝗽𝗹𝗲𝘀𝘁 𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀 For most production use cases: ↳ 𝗥𝗔𝗚 𝘄𝗶𝘁𝗵 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 > 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 (Tight retrieval, reranking, and schema constraints beat free-roaming planners.) ↳ 𝗛𝗲𝘂𝗿𝗶𝘀𝘁𝗶𝗰/𝗺𝗲𝘁𝗿𝗶𝗰-𝗯𝗮𝘀𝗲𝗱 𝗲𝘃𝗮𝗹𝘀 𝘄𝗶𝘁𝗵 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗴𝗿𝗼𝘂𝗻𝗱 𝘁𝗿𝘂𝘁𝗵 > 𝗟𝗟𝗠-𝗮𝘀-𝗮-𝗷𝘂𝗱𝗴𝗲 (Use the model to propose, not police, unless you’ve calibrated it carefully.) ↳ 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠 𝗮𝘁 𝘁𝗵𝗲 𝘀𝗲𝗮𝗺𝘀 > 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 (Let the LLM read/plan/rewrite; let code and tools execute.) ↳ 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗟 𝗼𝗿 𝗿𝘂𝗹𝗲𝘀 𝗳𝗼𝗿 𝘀𝘁𝗮𝗯𝗹𝗲 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 > 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗟𝗟𝗠 𝘀𝘁𝗼𝗰𝗵𝗮𝘀𝘁𝗶𝗰 𝗵𝗲𝗹𝗹 (Don’t use a bazooka to swat a fly; it's harder to aim) LLMs are powerful, but they’re one part of a disciplined software system. Engineer the system first. Insert the model where it actually improves reliability, speed, cost or efficiency. ♻️ Repost to share these insights.