AI Development Complete Guide
A practical roadmap from a measured workflow to evaluated models and production operations.
AI development connects a user workflow to data, models, software, and operations. The first decision is whether an AI component improves the task enough to justify its added cost and failure modes.
This resource hub covers language models, retrieval systems, deployment, and monitoring. The delivery plan below explains the evidence to gather at each stage; linked articles explore individual methods and industry applications.
Choose metrics for the task, establish data access and operating ownership, and test the simplest viable baseline. Treat rollout as a product decision supported by evaluation results rather than a model demonstration.
What You'll Learn
- LLM fine-tuning techniques including LoRA, QLoRA, and full fine-tuning for GPT, Llama, and Mistral models
- Retrieval Augmented Generation (RAG) implementation with vector databases and embedding strategies
- Production deployment using Docker, Kubernetes, and cloud platforms for scalable AI infrastructure
- MLOps best practices including CI/CD pipelines, model monitoring, and automated retraining
- Industry-specific AI implementations for healthcare, finance, retail, and manufacturing
- Advanced techniques including reinforcement learning, transfer learning, and federated learning
By TensorBlue Team · Updated
An AI development plan with evidence at every handoff
A useful project brief names one user task, its current baseline, the system that owns the data, and the consequence of failure. “Build an AI assistant” is too broad to test. “Draft a support reply from approved policy documents, with a reviewer approving the send” defines inputs, an output, and an operating boundary.
- Define the baseline. Measure how the task works today, including manual rules and existing software. Choose a business outcome and technical acceptance criteria. Record who can approve an exception and who will own the system after launch.
- Audit data readiness. Check access rights, provenance, missing fields, label consistency, and update frequency. Split evaluation data so near-duplicate records do not leak from training into testing. For changing processes, include a time-based holdout when appropriate.
- Prototype the simplest viable approach. Compare rules, conventional models, and language models on the same task. Inspect failures, not just averages. Reject an approach if its remaining mistakes exceed the workflow’s risk tolerance.
- Integrate one complete workflow. Connect authentication, data access, output validation, and the approval or fallback path. Test expired credentials, malformed inputs, upstream timeouts, and repeated requests. A model demonstration alone does not prove the workflow can operate.
- Pilot and operate. Release to a bounded cohort. Track quality by relevant segment, latency, operating cost, adoption, and overrides. Keep a rollback path and assign owners for incidents, retraining decisions, and changes to source data.
What belongs in the acceptance report?
Include the test-set version, baseline, candidate configuration, metric definitions, segment results, known limitations, and approval decision. A classification task may require precision and recall by class; a forecasting task may require error by horizon; a generated answer needs task-specific evidence and reviewer criteria. A single “accuracy” percentage cannot describe every application.
Google’s Rules of Machine Learning discusses starting simply, dependable pipelines, and training-serving skew. Use it as an engineering reference, then document the choices for your own product. For project risks and ownership, consult the voluntary NIST AI Risk Management Framework; using a framework does not certify a deployment.
Estimate scope after the risky assumptions are tested
Separate discovery, data preparation, evaluation, integration, rollout, and ongoing operations in the estimate. Identify dependencies such as vendor approval or missing labels. Re-estimate after the prototype shows the main failure modes; generic delivery multipliers and uptime promises are not substitutes for an agreed scope and service objective.
Technical and Industry Resources
LLMs & Fine-tuning
LLM Fine-tuning Complete Guide
Step-by-step guide to fine-tuning large language models for your use case.
LLM Quantization & Optimization
Compare compression methods and measure memory, latency, and task-quality trade-offs.
Prompt Engineering Best Practices
Master prompt engineering for GPT-4, Claude, and other LLMs.
LLM Fine-tuning Services
Plan training data, held-out evaluation, and deployment for your specific task.
RAG & Knowledge Systems
MLOps & Production Deployment
AI for Specific Industries
AI for Healthcare
Healthcare AI workflows, data handling, and implementation considerations.
AI for Finance
Fraud detection, risk assessment, algorithmic trading.
AI for Manufacturing
Quality control, predictive maintenance, optimization.
AI for Retail
Personalization, demand forecasting, inventory optimization.
AI for Education
Personalized learning, automated grading, student analytics.
AI for Legal
Contract analysis, legal research, compliance automation.
Advanced AI Techniques
Reinforcement Learning Guide
DQN, PPO, A3C for robotics, games, and optimization.
Transfer Learning
Adapt pre-trained models and compare the result with a task-specific baseline.
Federated Learning
Privacy-preserving ML for healthcare and finance.
Multi-Modal Learning
Combine vision, text, and audio for advanced AI.
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