Making LLMs Reliable for the Enterprise: Dhanunjay Mamidi’s Approach to Context and Validation

As LLMs shift from experimental use to realworld production a new challenge has emerged reliability These systems can produce helpful outputs that often seem just like human input However when these outputs are used in actual software their behavior can become unpredictable For engineering teams the main concern is no longer if AI can generate results but whether those results can be trusted in complex interconnected systemsDhanunjay Mamidi has tackled this problem by looking at the system as a whole Instead of seeing reliability as just a model issue he studies how AI outputs are managed understood and checked once they are part of a production workflow This move from focusing on generation to focusing on control is central to his approachThe Gap Between AI Output and System BehaviorLarge language models work well in controlled environments They answer prompts create structured outputs and adjust to context in a single exchange But enterprise systems bring in challenges that go beyond these simple settings Outputs need to work with existing code fit business rules and function in systems that rely on consistencyMamidi found that many failures in AI workflows come from a gap between what the AI produces and how the system behaves Often generated responses are seen as finished without enough checking or understanding of the larger system they are used in This leads to a mismatch between the modelrsquos output and the systemrsquos needsAs systems grow this gap becomes harder to manage For instance one output might start several downstream processes or cause hidden inconsistencies Traditional testing which relies on fixed inputs and expected results is not built to handle this kind of variabilityMamidi addresses this by treating AI output as just one step not the end result The goal is to make sure outputs are understood in context and checked before being usedWhy Prompting and FineTuning Fall ShortMany efforts to make AI more reliable focus on improving the model itself Methods like prompt engineering and finetuning help guide outputs to be more accurate or structured While these can help in simple cases they do not fully solve how outputs act when used in bigger systemsMamidi recognized that the limitation is not solely within the model but in the absence of control mechanisms around it Even wellstructured outputs can lead to unpredictable outcomes if they are applied without validation or awareness of system dependenciesThis difference is important Making outputs better does not always mean they will work reliably in practice Enterprise systems need outputs that can be trusted in many situations which goes beyond just improving the modelA SystemLevel Approach to AI ReliabilityMamidirsquos approach is to add structure around AI outputs instead of trying to remove all variability He sees AI as just one part of a bigger system and focuses on how outputs are handled after they are created His experience with backend systems and enterprise infrastructure shows that reliability comes from managing how changes move through the system not from removing all variation Using this idea Mamidi built a framework with two main parts keeping context and checking outputs before they are usedThe Role of ContextA main limitation of large language models is that they do not keep context between interactions They can handle information in one prompt but they do not naturally remember details across several steps in a workflowMamidi solved this by adding a memory layer that helps the system keep track of context across different steps This layer lets the system remember important information so outputs are understood in a consistent way instead of on their own This helps decisionmaking stay connected and outputs become part of a continuous process that takes into account earlier inputs the systemrsquos state and what is expectedThis is especially important in enterprise settings where workflows have many steps and need consistent understanding at each stage By keeping context the system can create outputs that better match how the application should workValidation Before Execution Establishing ControlContext alone is not sufficient to ensure reliability however Outputs must also be evaluated before they are applied within a systemTo solve this Mamidi created a validation layer that checks outputs before they are used This layer makes sure each output matches what the system expects follows business rules and fits operational limits Instead of assuming outputs are correct the system checks them first This control is key for using AI in production where mistakes can spread quickly and cause bigger problemsThe validation layer does not replace current testing methods Instead it adds to them by focusing on AIgenerated outputs It makes sure these outputs meet system requirements before they affect other parts of the processFrom Experimentation to InfrastructureTogether the memory and validation layers represent a shift in how AI is integrated into software systems Instead of treating AI as an isolated capability Mamidirsquos approach embeds it within a structured framework that governs how outputs are generated interpreted and appliedThis change lets AI move from just being tested to becoming part of core infrastructure Systems using AI need to be as consistent as the rest of the application By adding context and validation Mamidirsquos work helps achieve this consistency while keeping the flexibility that makes AI usefulThis approach also shows a wider view of how modern systems change As software gets more connected and flexible reliability comes from working together across parts not just controlling one area AI should be part of this structure not treated as something separateIteration as a Path to Practical SolutionsThe development of these systems did not follow a linear path Mamidirsquos process involved testing multiple approaches many of which revealed limitations when applied to realworld environmentsEarly solutions that worked well in simple tests often did not handle the complexity of enterprise systems Instead of just improving these ideas in isolation Mamidi focused on quickly testing reviewing and adjusting based on real results This helped the team see which methods worked reliably and which needed changes Over time this led to a framework that better matches how systems work in practicePositioning Within an Evolving FieldThe challenge of making AI reliable within enterprise systems is becoming increasingly important as adoption continues to grow And organizations are moving beyond experimentation and integrating AI into core workflows where consistency and predictability are essentialMamidirsquos work tackles this shift by focusing on context validation and systemlevel integration He has helped create a more structured way to make AI reliable matching what modern software needsHis perspective positions reliability not as a limitation of AI but as a design problem that can be addressed through architecture and workflow designA Framework Built on ExperienceMamidirsquos work in AI reliability is grounded in a broader track record of building and scaling systems under realworld constraints Earlier in his career he led the development of an internal brokerage calculator at Socieacuteteacute Geacuteneacuterale replacing a thirdparty tool and reducing operational costs by approximately one million dollars annually He later developed and scaled backend infrastructure for SidsFarm during a period of rapid growth supporting the company as it expanded its operations significantlyAs CoFounder and CEO of Covlant he has used this experience to build a company focused on better ways to validate software systems He led the development of the platformrsquos main technology guided its growth through many changes and built a team ready for a fastchanging industryHis work reflects a consistent focus on identifying structural problems within complex systems and developing solutions that address them with precision In the context of AI this has meant moving beyond generation and toward control and ensuring that outputs can be trusted within the systems that depend on themAs AI becomes a bigger part of software development the need for reliable contextaware systems will keep growing Mamidirsquos work offers a framework to meet this need based on both technical knowhow and realworld experience

Making LLMs Reliable for the Enterprise: Dhanunjay Mamidi’s Approach to Context and Validation

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Making LLMs Reliable for the Enterprise: Dhanunjay Mamidi’s Approach to Context and Validation

Other Articles

Mid-Day Fast