The 4D Framework (Discover, Distill, Deploy, Deliver)
Discover the 4D framework that structures the LLMOps lifecycle into four critical phases: Discover defines the problem and data; Distill refines prompts and validates quality; Deploy ensures scalability and cost control; Deliver monitors live systems for ongoing improvements. Learn the quality gates essential for advancing through each phase and maintaining reliable, secure LLM systems in production.
Consider this scenario. We built a chatbot that works most of the time.
We noticed a bug where the system misinterprets a specific question, so we tweaked the system prompt. Now it answers that question perfectly, but its accuracy on other questions drops. We tweak the prompt again, change the model temperature, and switch from OpenAI to Anthropic.
Six weeks later, we still have a demo. It works in the sense that it responds, but we can’t confidently ship it. We are not sure what standard of quality is expected; we don’t know what is good enough, and every change creates new uncertainty.
This endless cycle is called the eternal prototype syndrome.
In traditional software, we have clear stages: development, testing, staging, and production. In GenAI, the lines are blurry. Prompt engineering resembles coding, but it occurs in the midst of operations. Evaluation feels like testing, but it never really ends.
To ship reliable systems, we need a map.
We need a framework that clearly indicates when we have completed one phase and are allowed to proceed to the next. This methodology transforms the vague goal of building an AI agent into a series of engineered phases, each producing specific artifacts and gated by rigorous exit criteria.
In this lesson, we will explore the 4D framework in detail. We will see exactly what happens in each stage and, most importantly, the quality gates you must pass to move to the next.
Stage 1: Discover
Discover is the product definition phase.
Before designing prompts or selecting a model, we identify the real problem, confirm that an LLM is the right tool for the job, and verify that the required data is available and can be used safely. Many LLM projects fail because teams start with a preferred solution instead of a clearly defined user problem.
Many RAG projects fail for an even simpler reason: the data is messy, inaccessible, or restricted.
A practical way to think about discover is that we are mapping the full contract of the system. What questions should it answer, what questions must it refuse, what data is it allowed to see, ...