u/buiphobert

been thinking about this a fair bit lately. the use cases where LLMs genuinely simplify things seem to cluster around text-heavy, unstructured stuff:, pulling fields out of contracts, triaging support tickets, converting data between formats, that kind of thing. once there's ambiguity or judgment involved, an LLM handles it way better than trying to hardcode rules that break every two weeks. the inbox automation post on here recently kind of nailed it - deterministic tools doing judgment jobs is where things fall over. the pattern that actually works in production seems to be hybrid: traditional workflow engine, for reliability, permissions, and audit trails, LLM for the classification and routing and exception handling. not LLMs replacing everything, just slotting into the parts where rigid logic fails. worth noting though - even in those slots, you still need schema validation and human review on anything high-stakes. LLMs are still probabilistic and will hallucinate, so treating their output as a confident final answer without checks is where things quietly go sideways. workflow decomposition helps heaps too - breaking it into small schema-validated steps rather than one big prompt trying to do everything at once. reduces hallucinations and makes it way easier to debug when something goes wrong. with agentic setups getting more mature now, adding branching, loops, and human-in-the-loop checkpoints at the right spots makes a real difference for reliability. the build vs buy question is where i'm less sure. for anything regulated, high-stakes, or touching data sovereignty concerns, custom pipelines still seem like the, safer move - compliance and privacy requirements alone can rule out a lot of off-the-shelf options. but for most standard stuff n8n or similar gets you there faster. curious whether people here are finding the out-of-the-box tools actually hold up at scale or if you eventually hit a wall and end up building custom anyway.

been dealing with this more lately with a couple of clients who have brand names that overlap with generic category terms. the issue isn't new but with AI Overviews mixing informational, shopping, and recipe intent all into one SERP, it's gotten heaps messier. what's been working for us is building out really specific use-case content that ties the brand name to a concrete problem, not just the category. comparison pages, pricing pages, specific case studies. stuff that signals to Google 'this is a company, not a concept.' Organization schema with sameAs links pointing to LinkedIn, Crunchbase, etc. seems to help too, though honestly the results are inconsistent and take a while to show up. the bigger debate I keep running into is whether this is fundamentally a content/PR problem or a technical one. my gut says both matter but in different ways. schema helps Google understand what the entity is, but if the broader web doesn't consistently describe the brand the same way, you're kind of fighting uphill. curious if anyone's actually cracked this for a brand with a really ambiguous name, like not just improved things a bit but actually got clean branded SERPs. and does the same approach hold up in AI Overviews or does that need a totally different play?

been thinking about this a lot after seeing a client site sitting at position 1 on Google but basically invisible in ChatGPT answers. the opacity thing is real - you genuinely can't reverse-engineer why one source gets cited over another, and citation rates vary wildly, depending on the platform, query type, and even which model or interface you're using, so there's no clean benchmark to anchor to. and even when you do get cited, you're not necessarily getting much traffic out of it, since a lot of these answers are designed to keep people right there in the chat. the shift that's messing with my head is how these tools are functioning more like answer and recommendation engines than traditional search. people aren't always clicking through to verify - a lot of them are just taking the answer and moving on. so "ranking" means something different now, though I'd push back on my own framing a little:, Google rankings still matter because a lot of AI systems are pulling from high-authority, well-structured pages anyway. it's not like classic SEO is dead, it's more like the goalposts moved. what I've been leaning into is entity authority, brand presence, and making content genuinely extractable - clear structure, strong headings, schema, FAQs, solid E-E-A-T signals, third-party mentions. but it still feels like educated guesswork because nobody fully knows the selection logic and, the platforms keep changing fast enough that whatever you test today might not hold next month. curious whether others are treating AI visibility as a separate workstream from traditional SEO or still bundling it together, because I'm not convinced the same levers move both.

[ Removed by Reddit on account of violating the content policy. ]

been thinking about this a fair bit lately. running local models (ollama, vllm, whatever) is great for internal tooling and RAG pipelines, but as far, as I can tell it has basically zero effect on whether ChatGPT or Perplexity actually cites your content. GPTBot and ClaudeBot are crawling your public HTML, not caring what's running on your backend. the stuff that seems to actually move the needle is more on the content/infra side. SSR over SPA, schema markup, keeping content fresh. static sites apparently index way better in Perplexity compared to JS-heavy SPAs. llms.txt has been around for a couple years now at this point but still worth implementing if you haven't gotten around to it yet. where local LLMs do genuinely help is simulating queries for your own AEO research, like, spinning, up a local model to test how well your content actually answers specific questions before you publish. that's a legit use case. and with MoE-based models being so much more efficient to run locally now, you can do that kind of query simulation at scale without it being a pain. that said, AI referral traffic is apparently up something like 123% YoY at this point, so, the stakes for getting citation-worthy content right are way higher than they were even a year ago. curious if anyone here has actually tested server-side stuff and seen it change citation rates, or if the consensus is just "crawlability and schema, full stop."

been thinking about this a lot lately working with some local clients. the shift from "best pizza nyc" to "what's the best pizza place open near, me right now" sounds small but it completely changes how you need to structure content. AI assistants basically pick one answer and move on, so if you're not that one answer you're invisible. the debate i keep running into is where to prioritize first. Google Business Profile feels like the obvious foundation, but i've seen businesses with really solid GBP setups still, getting skipped in AI Overviews because their schema is a mess or their NAP data is inconsistent across directories. those two things can quietly tank you even when your profile looks great on the surface. FAQ content targeting conversational queries also feels criminally underrated for local AEO. like, most local sites aren't doing it at all, and it's one of the cleaner ways, to match how people are actually asking questions to voice assistants and AI search right now. so what's actually been moving the needle for local AI visibility for you all? curious whether GBP-first is still the right call as the starting point or if getting structured data and NAP consistency locked in should come before that. feels like the answer might just be "both at the same time" but would love to hear what's working in practice.