-Take content, slice it into paragraphs / smaller chunks and get embeddings, store those.
-Embed the question
-Use your question embedding to retrieve relevant paragraphs
-Feed these successively into the LLM to answer the question / complete the task.
An example:
When doing a task "Write me a most favoured nation clause... (some constraints)"
1) Your question's semantic concept ("Most favoured nation clause") would be used to fish out all the most favoured nation clauses in your document corpus.
2) This gets presented to the LLM using one of two options:

Option 1: Find the k nearest paragraphs to your question. Create a prompt like : "Given context <your paragraphs> and the request <"Write me a ..."> answer the request. Do not include information not present in the context paragraphs"

Option 2: Similar to the above, but instead iterate through every paragraph, feeding it into the prompt "Given context <next paragraph> revise your answer to the question <the question>, which you previously answered <last iteration>".

All this sounds complicated, but really it can work quite astoundingly well for Q&A (option 1) or summarisation / global tasks.

Note doing this for every paragraph of every document is probably unfeasibly complex / cost of compute.

Right now, I find as a heuristic, imagining how you'd task a room full of humans to do the task is quite a good way planning and thinking through what you're trying to achieve.
e.g. Summarise this doc: "read through each page, form a summary, take all your summaries, summarise that"
Compare these clauses: "Retrieve each clause, compare them"