Generative AI Project Lifecycle-GENAI On Cloud

TL;DR

Start by defining the GenAI use case (RAG, summarization, chatbot) and scope the required components and data needs.

Briefing Cornell Notes

Briefing

Generative AI projects on cloud follow a repeatable lifecycle: define the business use case, choose and adapt the right model, evaluate it, then deploy and integrate it into applications. The practical payoff is straightforward—teams can move from raw requirements to an inference-ready system without skipping the steps that usually cause quality, cost, or latency problems.

The lifecycle starts with use-case definition. Whether the target is a RAG application, a text summarization system, or a chatbot, the work begins by scoping what the solution must do and what data and components it needs. For example, a RAG workflow typically requires converting documents (like PDFs) into embeddings and storing them in a vector database so the system can retrieve relevant context at runtime.

Next comes the most consequential decision: selecting the right model approach. Teams can either use foundation models—large prebuilt models such as OpenAI, Llama 2, Llama 3, or Google’s Gemini Pro—or build a custom LLM from scratch. Foundation models can also be adapted further using fine-tuning so the model behaves better on a company’s specific data. Building a custom LLM can deliver tighter control for specialized use cases, but it demands significant resources and careful handling of issues like hallucinations.

After model selection, the workflow shifts to adapting and aligning models through three main techniques: prompt engineering and prompt-based solving, fine-tuning, and training with human feedback (a key method for teaching models to follow instructions and reduce undesirable outputs). Once adaptation is done, evaluation becomes the gatekeeper. Performance metrics must show improvement—only then does the model qualify as “ready” for real-world use.

Deployment is where cloud engineering turns a working model into a usable service. Deployment requires integration with applications and optimization for inference speed and reliability. This is also where LLM Ops enters the picture, since it supports production-grade inference patterns and operational concerns. The transcript emphasizes that multiple inference strategies matter because a model that isn’t fast enough can’t serve users effectively. The focus is initially on AWS, with mention that Azure and GCP also provide services for inference, and that teams should learn the options each cloud offers.

Finally, once APIs and integration are in place, the project moves into building LLM-powered applications—turning the deployed model into end-to-end solutions that solve the original business problems. The overall message is that cloud-based GenAI development isn’t just about training or fine-tuning; it’s about building a pipeline from use-case scope to inference-ready deployment and application integration, with evaluation and LLM Ops treated as first-class steps.

Cornell Notes

The GenAI project lifecycle on cloud is organized into a sequence: define the use case, choose the right model strategy, adapt/align it, evaluate performance, then deploy and integrate it into applications. Use-case scoping determines whether the system is RAG, summarization, or a chatbot, and RAG typically requires embedding documents and storing them in a vector database. Model choice can rely on foundation models (with optional fine-tuning) or on building a custom LLM from scratch, which is resource-intensive and requires managing issues like hallucinations. Adaptation and alignment typically use prompt engineering, fine-tuning, and training with human feedback. After evaluation confirms improved metrics, deployment focuses on inference optimization and LLM Ops, followed by application integration and building LLM-powered products.

How does defining the use case shape the rest of a GenAI project lifecycle?

Use-case definition sets the scope and determines the technical components needed. A RAG application, for instance, implies building a retrieval pipeline: converting PDFs into embeddings and storing them in a vector database so the system can retrieve relevant context. A chatbot or text summarization system may not require the same retrieval storage layer, but it still requires clear requirements that guide model selection and evaluation targets.

What are the two main paths for model selection, and when does each make sense?

Model selection splits into using foundation models versus building a custom LLM from scratch. Foundation models (e.g., OpenAI, Llama 2, Llama 3, Google Gemini Pro) are suitable for many generic use cases because they can solve tasks directly. Fine-tuning can further adapt them to company-specific data. Building a custom LLM from scratch can fit specialized needs but requires substantial resources and increases the need to manage model hallucination and other quality risks.

Which techniques are used to adapt and align models after choosing them?

Adaptation and alignment typically involve three techniques: prompt engineering (and prompt-based solving), fine-tuning, and training with human feedback. Prompt engineering helps steer behavior without changing model weights. Fine-tuning updates the model using task-specific data. Training with human feedback is used to improve instruction-following and reduce undesirable outputs, making it a key step in training pipelines.

Why does evaluation come before deployment, and what does it measure?

Evaluation acts as a readiness check. After adaptation, teams run performance metrics to confirm that quality improves—such as accuracy, relevance, or other task-specific measures—before moving forward. The lifecycle treats rising metrics as the condition for declaring the model ready for deployment rather than assuming improvements without measurement.

What does deployment require in cloud GenAI systems, and why is inference optimization central?

Deployment requires application integration and model optimization for inference. The goal is to make the model fast and reliable enough for real-time or near-real-time use. LLM Ops supports production inference operations and inference techniques. The transcript stresses learning multiple inference approaches because latency and throughput determine whether the system can actually be used by end users.

After deployment, what work remains to turn a model service into a working product?

Once deployment provides an API and the model is integrated, the next step is building an LLM-powered application. This is where teams assemble the solution logic around the deployed model—connecting user workflows to the inference service so the system solves the original use case (RAG, summarization, chatbot) end to end.

Review Questions

What decision points in the lifecycle determine whether a RAG system needs vector database storage and embedding pipelines?
Compare foundation model fine-tuning with building a custom LLM from scratch in terms of resource demands and quality risks.
Why must evaluation metrics improve before deployment, and how does inference optimization affect real-world usability?

Key Points

1
Start by defining the GenAI use case (RAG, summarization, chatbot) and scope the required components and data needs.
2
Choose between foundation models (optionally fine-tuned) and custom LLMs built from scratch, balancing capability against cost and risk.
3
Adapt and align models using prompt engineering, fine-tuning, and training with human feedback.
4
Evaluate with performance metrics and only proceed when results show measurable improvement.
5
Deploy by integrating the model into applications and optimizing for inference speed and reliability.
6
Use LLM Ops to support production-grade inference operations and operational workflows.
7
After deployment, build the LLM-powered application layer that turns APIs into end-to-end solutions.

Highlights

The lifecycle treats evaluation as a hard gate: improved metrics are the condition for readiness before deployment.

Inference speed is framed as non-negotiable—models must be optimized for fast inference to be usable.

Model strategy splits cleanly into foundation models (with optional fine-tuning) versus custom LLMs built from scratch.

Deployment is paired with application integration, and LLM Ops is positioned as the production backbone for inference.

RAG is tied to a concrete pipeline: document-to-embeddings conversion and vector database storage.

Topics

GenAI Project Lifecycle
Use Case Definition
Model Selection
Model Adaptation
LLM Ops Deployment
Inference Optimization

Mentioned

Krish Naik
LLM Ops
RAG
API
LLM