Not as Easy as it Looks

You've seen the demos, experienced the hype and been made the outrageous promises but how hard is it really to build enterprise-read GenAI agents? Here, we outline a structured approach to building a Retrieval Augmented Generation (RAG) Pipeline for enterprise use, highlighting the realistic time frames and hurdles that organizations may encounter along the way.

Hypothesis and Use Case Workshops (1 month)

The journey begins with hypothesis formulation through immersive use case workshops. These sessions serve as the foundation for understanding business needs, prioritizing objectives, and outlining the initial business case. Teams collaborate to develop straw-man solution hypotheses, setting the stage for subsequent development iterations.

Building the Team (3 - 6 months)

Success in implementing a RAG Pipeline hinges on assembling the right talent. This entails either hiring a specialized full-stack development team coupled with GenAI specialists or investing in upskilling existing staff members. Given the complexity of the task, this phase typically spans three to six months, ensuring the team is equipped with the necessary skills and expertise.

Proof of Concept (1 month)

With the team in place, the focus shifts to building out the foundational elements of the RAG Pipeline. This includes establishing the base infrastructure encompassing hosting, LLM embeddings model, and a robust vector database. Additionally, a platform is developed to seamlessly integrate open-source retrieval, query, and ingestion engines. This is the stage most GenAI projects have found themselves at and its easy to look at your proof of concept and think you're most of the way there - unfortunately, there's still a long way to go!

Architecture Design (3 months)

The architectural phase is pivotal in ensuring the reliability, scalability, performance, cost-effectiveness, and security of the GenAI application. Over the course of three months, the team will have to meticulously craft an architecture blueprint, paving the way for deployment into live environments. Moreover, robust deployment pipelines, testing frameworks, and operations strategies are established, along with requisite tooling to support ongoing maintenance.

Evaluation & Monitoring (2 months)

The journey towards operational excellence demands continuous evaluation and monitoring. During this phase, open-source evaluation and monitoring services are integrated, aligning metrics with business objectives to monitor quality and consistency across all facets of the process. Rigorous testing samples are manually executed across diverse use cases, ensuring the efficacy of the RAG Pipeline.

Security Integration (2 months)

Safeguarding sensitive data and ensuring regulatory compliance are paramount concerns in enterprise AI deployments. Hence, this phase entails the integration of open-source guardrails frameworks and the implementation of robust mitigations against data sovereignty risks. Over the course of two months, security protocols are meticulously woven into the fabric of the RAG Pipeline, fortifying its resilience against potential threats.

Integration with Related Applications (1 month)

The final stretch involves seamlessly integrating the RAG Pipeline with existing enterprise applications. This entails building robust integrations, while striking a delicate balance between deployment frequency and risk management. Within a month, clarity is attained over deployment controls, ensuring flexibility without compromising operational integrity.

Multi-Agent Environment Considerations

For organizations venturing into multi-agent environments, the journey is further compounded. Each additional agent necessitates a repetition of the entire pipeline construction process. Moreover, the intricacies of agent interactions require careful consideration, with changes managed through code to maintain coherence and stability across the ecosystem.

Conclusion

Once you have budget and a business case approved, it is important to understand that your GenAI journey is likely to be 12-18 months from start to finish. Moreover, even if you have your money, your team and your proof of concept developed, you're likely still 8-12 months away for having a live system securely deployed for enterprise use. This of course all assumes use of open source components. If your business will not accept open source, many of the timings above will double.

While the prospect of leveraging RAG-powered GenAI agents for enterprise operations is undeniably enticing, it's imperative to approach the journey with a clear-eyed understanding of the challenges and timelines involved. By embracing a structured approach and navigating the complexities with diligence and foresight, organizations can harness the transformative potential of AI to drive unparalleled innovation and efficiency in their operations.