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Comment by iandanforth | Hacker News Reader

I'll share my recipe for using these products on the off chance it helps someone.

1. Only do searches that result in easily verifiable results from non-AI sources.

2. Always perform the search in multiple products (Gemini 1.5 Deep Research, Gemini 2.0 Pro, ChatGPT o3-mini-high, Claude 3.7 w/ extended thinking, Perplexity)

With these two rules I have found the current round of LLMs useful for "researchy" queries. Collecting the results across tools and then throwing out the 65-75% slop results in genuinely useful information that would have taken me much longer to find.

Now the above could be seen as a harsh critique of these tools, as in the kiddie pool is great as long as you're wearing full hazmat gear, but I still derive regular and increasing value from them.

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Good advice.

My current research workflow is:

* Add sources to NotebookLM

* Create a report outline with NotebookLM

* Get Perplexity and/or Chatgpt to give feedback on report outline, amend as required.

* Get NotebookLM and Perplexity to each write their own versions of the report one section at a time.

* Get Perplexity to critique each version and merge the best bits from each.

* Get Chatgpt to periodically provide feedback on the growing document.

* All the while acting myself as the chief critic and editor.

This is not a very efficient workflow but I'm getting good results. The trick to use different LLMs together works well. I find Perplexity to be the best at writing engaging text with nice formatting, although I haven't tried Claude yet.

By choosing the NotebookLM sources carefully you start off with a good focus, it kind of anchors the project.

I should also mention that this more 'hands on' technique is good for learning a subject because you have to make editorial assessments as you go.

Maybe good for wider subject areas, longer reports, or where some editorial nuance helps.

> ... perform the search in multiple products

I do that a lot, too, not only for research but for other tasks as well: brainstorming, translation, editing, coding, writing, summarizing, discussion, voice chat, etc.

I pay for the basic monthly tiers from OpenAI, Anthropic, Google, Perplexity, Mistral, and Hugging Face, and I occasionally pay per-token for API calls as well.

It seems excessive, I know, but that's the only way I can keep up with what the latest AI is and is not capable of and how I can or cannot use the tools for various purposes.

This makes sense. How many of those products do you have to pay for?

I'm not OP but I do similar stuff. I pay for Claude's basic tier, OpenAI's $200 tier, and Gemini ultra-super-advanced I get for free because I work there.

I combine all the 'slop' from the three of them in to Gemini (1 or 2 M context window) and have it distill the valuable stuff in there to a good final-enough product.

Doing so has got me a lot of kudos and applause from those I work with.

Wow, that's eye-opening. So, just to be clear, you're paying for Claude and OpenAI out of your own pocket, and using the results at your Google job? We live in interesting times, for sure. :)

No no, that would get me fired.

So you are basically doing a first pass with diverse models and second pass catches contradictions and other issues? It could help with hallucinations.

For which work tasks do you find this workflow useful, given that you can't feed confidential information into the non-Gemini models?

I do that for non-work tasks, like comparing notes on financial schemes or career growth ideas.

For work tasks, there are several different variants of Gemini that are tuned for different things, just as OpenAI has.

> comparing notes on financial schemes or career growth ideas

I'll admit I'm surprised you need to combine all these LLMs to get a decent result on this kind of queries but I guess you go deeper than what I can imagine on these topics.

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