Building and Fine-Tuning LLM Applications

Packt connects application development with two tuning sections covering hosted models and LoRA-based workflows. RAG and LangChain examples provide broader application context. It suits experienced programmers who want to connect customization with a usable interface, while keeping retrieval quality, model behavior and deployment concerns as separate things to evaluate.

What you will learn

• Connect RAG and LangChain application workflows.
• Explore hosted and LoRA-based tuning.
• Test an API for a customized model.

Who this suits

Python and LLM familiarity are expected; machine-learning and LangChain foundations are helpful.

Before you enroll

Training may require paid APIs, suitable GPU hardware or cloud credits. Check current model availability, licenses and library compatibility before enrolling. Use permissioned, non-sensitive datasets and keep evaluation examples separate from training data. Compare the tuned model with the baseline; tuning does not guarantee better accuracy or reliable behavior.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Define one fictional application task and explain whether its failure calls for better retrieval, a different prompt or tuning before selecting a training approach.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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