ChatGPT Mastery: Prompt Engineering and RAG with Chat GPT-6

About this course

Make your ChatGPT prompts more deliberate and your retrieval-augmented generation answers easier to check. This course combines prompt-engineering explanations, language-model concepts and RAG demonstrations with practical reading workshops, including a GPT-6 guide.

Start with instructions, context, response format and common prompting pitfalls. Explore NLP topics such as tokenization, sentiment analysis, named entity recognition and summarization, then examine how retrieval supplies external information to an answer. Demonstrations introduce LangChain, Langflow, LM Studio and Gemini workflows alongside the recorded ChatGPT material.

Work on skills you can evaluate

Describe a task, its constraints and a useful output format.

Compare prompt variations against the same examples.

Organise source documents so answers can be checked against evidence.

Distinguish a retrieval problem from an unsupported claim in the final response.

The final workshops ask you to build a fictional source pack, identify conflicting document versions and test answers claim by claim. The dedicated GPT-6 article covers model choice, outcome-focused prompts, tool boundaries and a study-assistant experiment. GPT-6 coverage is provided in this text lesson; it does not mean the earlier videos have been re-recorded.

Choose this course if you want a structured introduction to prompting and source-aware AI workflows for writing, learning, research or software tasks. Coding familiarity is helpful for integrations. Some videos show earlier interfaces and historical plugin examples, so check current documentation when reproducing them. Model access, external software and provider charges are separate from enrollment. The emphasis is a repeatable way to improve and assess a workflow, rather than collecting prompts whose results you cannot explain.

A course built around better questions and checkable answers

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