I’m working on a project that depends on accurate question and answer AI tools, but I’m having trouble finding the most reliable options. I’d really appreciate recommendations or insights from anyone who’s used AI tools for this purpose. Need something user-friendly and effective for handling diverse questions.
Alright, let’s get into it: If question-answering is your jam, then there’s a short list that really rolls with the best of 'em right now. OpenAI’s GPT-4 (what powers this) is honestly one of the most robust for general-purpose Q&A—super flexible, handles context well, and it’s trained on a metric ton of data (though, yep, hallucinations happen…you gotta double-check). Google’s PaLM 2 is another powerhouse, especially for answering factual stuff and it excels at some niche topics. For open-source, Llama 2 by Meta is surprisingly solid, but you’ll need to tweak and fine-tune it to match commercial options (and that’s some legwork, just sayin’).
For more plug-and-play options, Perplexity AI is simple but focuses on providing sources for its answers, which is a huge perk if you want citations. Bing AI (built on GPT-4) is fine if you need up-to-date stuff, since it can search the web live—rare for most LLMs that usually stagnate at a training cutoff. If you’re specialized (like, medical or law), check out specific vertical AIs like MedPaLM or LexisNexis’ solutions. They’re tuned for those domains.
Caveats: No model is ‘always accurate.’ Everyone gets tripped up on ambiguous questions, super-recent news, or when you’re asking for personal opinions framed as facts. Best bet: ensemble these tools when accuracy really matters. Double-check, use retrieval-augmented generation (e.g. LangChain/LlamaIndex frameworks hooked up to your database), and never trust just one answer—even if it sounds confident.
TL;DR? Go GPT-4 for general, Bing for up-to-date, Perplexity for citations, specialized models for niche, open-source if you wanna tinker/have privacy needs. And always verify; none of these are omniscient gods…yet.
People swear by GPT-4 (and with good reason), Perplexity’s pretty cool for sourcing, and Bing AI is that kid in class who always knows the latest gossip. But honestly? Sometimes these models just don’t GET IT, especially if you’re in a super-specialized field or your questions require anything beyond regurgitating internet facts.
Let me throw in a curveball: have you tried Claude 2 by Anthropic? I don’t see it mentioned as much, but for nuanced comprehension and a more “human” tone, it’s actually impressive. It stumbles less on passage reasoning IMO, though yes, price per token is still an issue. And if you want open-source but can’t hang with the Llama tuning headache @andarilhonoturno mentioned, try Mistral or Falcon—fewer bells and whistles, but easier spin-up.
Some folks go full nerd and use RAG (retrieval augmented generation), literally combining vector database search with LLMs for “grounded” answers (LangChain is hot for this). Downside: you become I.T. support for your own AI Frankenstein. Not everyone wants that.
Personal take—I wouldn’t trust JUST the AI for mission critical Q&A. Think AI-human tag team: AI drafts, human fact-checks. Also, for tight constraints, consider classic extractive readers like Haystack or even search APIs as backup.
TLDR: GPT-4/PaLM 2 for flex, Claude 2 and Falcon for alt approaches, Perplexity/Bing for transparency and recency. Don’t sleep on boring old retrieval systems if you just want “is it correct.” No magic bullet, but stacking tools and sanity-checking is the name of the game.
Quick rundown for anyone eyeing the Q&A AI tools buffet: You’re getting solid advice already, but let’s slice it from a slightly different angle. First up, OpenAI’s GPT-4—the current king for general-purpose tasks. Super adaptable, but true talk, it’s a black box: sources sometimes missing, and hallucinations aren’t rare, no matter how confidently it types. Comparable vibes with Google’s PaLM 2—maybe a little sharper on factoids, but both are cloud-locked and not ideal if you need granular customization or strict data privacy.
If transparency’s your jam, Perplexity AI (which gets a lot of nods above) is the only mainstream kid consistently offering actual sources, making it awesome for research-heavy stuff… though its native engine doesn’t push the “reasoning” envelope like GPT-4 or Claude 2. Still, citations = big win, huge for trust. Bing AI’s web integration is neat for cutting-edge news, but its “Bing-isms” (sometimes too literal, sometimes oddly off) might trip up nuanced answers. PaLM 2, if you can get access, is lightning-fast with retrieval but has a slightly more rigid tone.
Claude 2 is an interesting curveball—it just “gets” context better in some cases, and its passage reasoning feels less brittle. Downside: cost and sometimes, a stubborn refusal to answer “edgy” or complex stuff (Anthropic locks it down harder on some safety rails).
On the open-source front, Llama 2 (as already outlined) is worth a shot if you need local control, but bear in mind it’s not “plug and play”—prepping, training, and finetuning are required for peak performance. Mistral and Falcon deserve honorable mentions for speed and easy self-hosting.
Here’s where I half-disagree with the “always ensemble” approach. Sometimes, simple extractive tools like Haystack or ElasticSearch, paired with basic rules, outperform even fancy LLMs for data that’s highly structured or repetitively formatted. Don’t overlook boring old filters if all you want is “Did the user ask about X? Is the answer Y in the docs?” For critical queries, especially compliance/legal/medical, always fall back to human review, but if you’re rapid prototyping or handling generic questions, a tuned GPT-4 still gives you the best value-to-hassle ratio right now.
Best bet: stack GPT-4 for thinking, Perplexity for citations, Claude 2 for narrative/long docs, and classic search as backup. Don’t put all your trust in the AI’s poise—“looks right” is not “is right.”