## Overview This case study is drawn from a conference presentation by a team of engineers from Microsoft's Industry Solutions Engineering group, specifically DV, Hanan Buran, and Jason Goodell. The presenters emphasize that they are sharing their personal opinions based on field experience rather than representing official Microsoft positions. Throughout 2024, this team worked extensively with strategic enterprise customers in Australia, helping them develop and deploy large language model solutions. The presentation synthesizes their collective lessons learned into actionable guidance for teams beginning their LLMOps journey. ## The Mindset Shift Required for LLM Development One of the most fundamental points emphasized in this case study is the radical mindset change required when transitioning from classical software development to LLM application development. In traditional software engineering, applications behave deterministically—they do exactly what the code instructs and nothing more. LLM-based applications, however, operate on an inverse principle: they will attempt to do everything unless explicitly constrained not to. The presenters use a memorable analogy throughout the talk, comparing LLM applications to raising a puppy—they require constant attention, training, and supervision. This is not a "set and forget" paradigm like traditional software deployments. The non-deterministic nature of LLMs, where the same input can produce different outputs, creates unique challenges for testing, validation, and production monitoring that traditional DevOps practices don't fully address. ## Cross-Functional Team Requirements Drawing parallels to the DevOps transformation of the previous decade, the presenters stress that successful LLM development requires cross-functional teams with diverse skill sets: - **Engineers** provide the technical implementation capabilities - **Data Scientists** bring the scientific rigor needed to frame questions correctly and design proper experiments - **Subject Matter Experts** ensure solutions deliver genuine business value rather than becoming "gimmicky LLM applications" - **Platform Engineers** build the observability infrastructure and manage the operational aspects of running expensive LLM solutions in production This team composition reflects the reality that LLM applications sit at the intersection of software engineering, data science, and domain expertise in ways that previous technology generations did not. ## The Nature of Generative AI Applications Hanan Buran's section of the presentation delves into why LLMOps represents a distinct discipline from traditional DevOps, despite the similar-sounding definition. The key distinction lies in the unique characteristics of generative AI applications: **Non-deterministic behavior**: LLMs fundamentally work by predicting the next token—they "guess" rather than calculate deterministically. This means identical inputs can produce varying outputs, making traditional testing approaches insufficient. **Inherent bias**: The training data and methodology influence model outputs in ways that aren't always transparent or predictable. When asked "what is the best place to go on vacation," the model's preference for beach versus mountains reflects training biases rather than objective truth. **Context sensitivity**: The same question can yield dramatically different answers based on context. Adding that the person "likes snowboarding" fundamentally changes the model's response trajectory. **Data drift**: In RAG implementations, document updates or obsolescence directly impact model behavior. The knowledge base is a living entity that requires ongoing curation. ## Continuous Evaluation and Experimentation The presentation introduces a dual-loop framework for LLMOps that distinguishes it from traditional CI/CD: **Inner Loop (Experimentation and Evaluation)**: This is where the team establishes baselines and prevents regression. Without proper experimentation infrastructure, teams cannot distinguish between "the application works" and "the application works sometimes." The process involves running experiments against the application, establishing accuracy baselines (e.g., "80% accurate"), and ensuring updates don't introduce regressions. **Outer Loop (Data Collection and Feedback)**: Production data collection is critical because the questions in evaluation datasets rarely match the actual questions users ask in production. The presenters recommend multiple feedback mechanisms: - Thumbs up/thumbs down ratings on responses - A/B testing with alternative answer presentations - Tracking whether users copy response text (indicating utility) Automation is emphasized as essential—manual evaluation processes cannot keep pace with the rate at which LLM applications evolve and drift. ## RAG Implementation Challenges Jason Goodell's portion focuses specifically on Retrieval Augmented Generation, presenting a balanced view that counters the often oversimplified narratives around RAG adoption. While RAG offers a faster, cheaper path than training or fine-tuning custom models, it introduces its own set of complexities: **Data flux**: Knowledge bases change over time. What worked at initial deployment may fail 12 months later as documents are updated, retired, or added. **Chunking strategy evolution**: As LLM context windows and capabilities evolve, chunking strategies that were optimal at launch may become suboptimal, requiring reevaluation. **Data quality dependency**: The "garbage in, garbage out" principle applies forcefully. If internal data isn't properly structured, indexed, and curated for retrieval, the RAG system will underperform regardless of the sophistication of the retrieval mechanism. **Retrieval precision and faithfulness**: These metrics need evaluation early in the development process, not as an afterthought before production deployment. ### Recommended RAG Practices The presenters recommend using templated responses rather than passing raw LLM outputs directly to users. This mitigation reduces brand risk from edge cases where the model might "go off track" and produce inappropriate content about customers, competitors, or other sensitive topics. A cited example involves a car dealership chatbot that was tricked into agreeing to sell a Chevrolet for one dollar—an amusing anecdote but a serious business risk. ## Security Considerations and Attack Vectors The final portion of the presentation addresses security risks that are often overlooked in LLM deployments: **Jailbreaking**: Creative query formulations that bypass the safety constraints ("training wheels") built into models. This represents the entry point for most attack vectors against LLM applications. **Prompt Injection**: Attackers attempt to override system prompts with their own instructions. In RAG implementations, this is particularly dangerous because external content loaded for context enrichment can contain poisoned prompts. The presenters cite Slack's recent vulnerability where a plugin could load external content from areas the logged-in user shouldn't access, enabling attackers to exfiltrate API keys and private conversations. **Excessive API Permissions**: A common anti-pattern involves giving LLM applications broad API access on behalf of all users. If a jailbreak or prompt injection succeeds, attackers can instruct the application to access other users' data. ### Recommended Security Mitigations - Be explicit about which parameters the LLM generates versus which come from authenticated user context (e.g., extracted from JWT claims) - Pass authentication context through to backend APIs so they can enforce authorization regardless of LLM behavior - Build custom guardrails that function as middleware, checking each request for financial advice, PII exposure, competitor mentions, and other organization-specific concerns - Accept that multiple LLM calls per request may be necessary for comprehensive safety checking ## Key Takeaways for Enterprise Teams The presenters conclude with actionable recommendations: **Embrace the mindset change**: Accept that LLM solutions require constant attention rather than deployment and abandonment. **Start simple**: Avoid agentic frameworks like LangChain at the outset. The underlying operation is fundamentally a prompt going in and a response coming out—additional complexity should be "earned" as the solution matures, similar to the microservices adoption pattern. **Invest in LLMOps skills**: This represents the next evolution of DevOps and warrants deliberate skill development investment. **Implement RAG carefully**: It will likely be necessary (training and fine-tuning aren't suitable for rapidly changing data), but requires templated responses, proper data curation, and ongoing evaluation. **Guardrails are non-negotiable for enterprise**: The difference between tutorial implementations and production-ready enterprise solutions is comprehensive guardrails. Without them, solutions cannot responsibly be deployed to production, particularly in regulated industries like finance and healthcare. ## Practical Observations The presenters recommend against starting with agentic frameworks, noting that in their experience helping enterprise customers, the added complexity typically isn't justified until solutions reach a certain maturity level. They advocate for the same "earn your complexity" approach that guided the microservices adoption journey. For experimentation infrastructure, they emphasize the importance of labeled data and production data when available, as these enable tangible experiments rather than abstract evaluations. They also recommend implementing a "Gen Gateway" for organizations with multiple teams building generative AI solutions—a centralized, policy-based access layer that provides observability across workloads. The cost implications of LLMOps are briefly mentioned but emphasized as a critical consideration. Every LLM call adds cost and latency, making automation and efficiency crucial for sustainable production deployments.