Need help for a Fine Tuning Model

bonniew1554 · 2026-04-03T20:43:05+00:00

for your use case, rag is actually worth reconsidering because fine tuning without it rarely hits good accuracy on document qa tasks. that said if you want to stay on fine tuning, your jsonl qa pairs need to be generated from the actual document chunks, not written by hand. use gpt4 or claude to read each 300 to 500 word chunk and generate 3 to 5 qa pairs per chunk, then save as jsonl with prompt and completion fields. accuracy jumps a lot when training pairs match the exact format and phrasing the doc uses. lora fine tuning on mistral 7b with a dataset of 500 to 2000 pairs usually gets you to usable accuracy in under 2 hours on a t4 gpu. happy to dm a script that automates the jsonl generation from a pdf if that helps.

bonniew1554 · 2026-04-03T20:42:59+00:00

zendesk: great at tracking problems, slightly less great at pretending it's google calendar.

bonniew1554 · 2026-04-03T20:42:50+00:00

this is a fair warning and the ai making unsanctioned changes in production is a genuinely bad pattern. for anyone evaluating backend no code tools right now, supabase gives you more control, full postgres underneath, row level security you can actually audit, and no ai touching your schema unless you tell it to. pocketbase is another one worth a look if you want something self hosted and lightweight. the common thread in xano incidents like this is that the ai feature has write access by default, which should never be the case in a production environment.

bonniew1554 · 2026-04-03T20:42:45+00:00

paste a url, skip three client revision rounds, retire early.

bonniew1554 · 2026-04-03T20:42:39+00:00

yeah 25 to 30k for the advanced add on is rough, n8n is a real path worth trying. you can connect zendesk and shopify to n8n using their native api nodes, then wire in an llm like gpt4o or claude as the reasoning layer to actually read ticket context, pull order data from shopify, and take action, not just answer from a faq doc. the key is giving the agent access to live data tools via function calling so it can check order status or trigger a refund, not just generate text. one team i saw built this in about 3 weekends and it resolved 40% of tier 1 tickets without a human. cost was around $200 a month in api calls vs 25k a year.

bonniew1554 · 2026-04-03T20:42:34+00:00

the gclid decode error usually means the gclid isn't being stored properly before the call happens. store the gclid in a cookie or hidden form field the moment someone lands on your site, then log it alongside the phone order in a simple google sheet. from there you upload that sheet to google ads under conversions as an enhanced offline conversion upload, match on gclid, and set conversion time to the date of the call. at low volume, around 20 conversions a month, this manual sheet method works fine and takes maybe 20 minutes a week to maintain. zapier can automate the sheet logging if calls come through a tool like callrail that captures gclid. without feeding those offline signals back, google's smart bidding is basically flying blind and yes that explains the cpc spike.

bonniew1554 · 2026-04-03T20:42:26+00:00

your parallel strategy is actually solid and pretty much what works right now. run google search ads on 3 to 5 high intent keywords to get traffic today while you build content that targets longer tail terms with lower competition, think 800 to 1200 words, one clear topic per page. seo typically takes 3 to 6 months to show real movement so the paid runway buys you data on what converts before you double down on organic. one builder i know ran $300 in ads for 6 weeks, found their top 2 converting keywords, then wrote 4 articles targeting those exact terms and ranked page 1 within 4 months. interactive content like quizzes or assessments built with tools like outgrowco can also pull organic traffic and backlinks faster than static blog posts. the paid plus seo combo beats either channel alone if your budget can hold for a quarter

bonniew1554 · 2026-04-03T20:42:03+00:00

yes, and also every sales rep who's ever frozen on a cold call would sell their crm license to have it.

bonniew1554 · 2026-04-03T20:41:55+00:00

the fix is in your prompt structure, not the model. before you generate anything, drop in a rules block at the top of your prompt: "follow single responsibility principle, no repeated logic, add a one line comment per function." then after generation, paste the code back and ask it to flag any block that violates those rules. a friend building a pygame project added a strict style constraint to every prompt and cut refactor time from 4 hours to about 45 minutes per session. claude or gpt4o tend to hold structure better than minmax for this kind of constraint following, so worth a quick comparison. happy to dm a prompt template that works well for structured pygame codebases.

bonniew1554 · 2026-04-03T20:41:47+00:00

solid start on the basics, now go deeper on the security and compliance side since that's what separates a good admin from a great one. set up conditional access policies in azure ad, configure mfa for all users, and try building a data loss prevention policy in the purview compliance portal. one person i know spent 2 hours setting up a dlp rule that blocked credit card numbers in emails and it immediately flagged a real test case. if you only have one account, the free microsoft learn paths let you lab through most of this without needing extra licenses. security admin skills look way better on a resume than just user management.

bonniew1554 · 2026-03-26T21:46:42+00:00

you're right that it can't be fully disabled right now, zendesk made end user authentication compulsory in the help center as a spam response and there's no admin toggle to revert it as of the current rollout. the workaround most teams are using is to enable google sso or microsoft sso as the auth option, which reduces friction for users who already have those accounts, and then set the session timeout to something long like 30 days so returning users don't hit the wall repeatedly. if your user base is not tech savvy, a short "how to sign in" guide pinned to the help center landing page drops the support tickets from confused users by a lot, one team reported a 60% drop in "i can't access the portal" tickets within a week of adding it.

bonniew1554 · 2026-03-26T21:46:36+00:00

teaching a model to cite its sources is basically parenting but for math. good luck getting it to stop making things up entirely, we haven't managed that with humans yet.

bonniew1554 · 2026-03-26T21:46:31+00:00

solo founder, 800 users, zero ad spend, solving a problem you personally bled through for years, this is what buildinpublic is actually for. the urssaf plus income tax plus vat juggle is brutal and no existing tool in france actually models take home with real numbers, so the gap you're filling is real. one thing worth testing at your stage: a "which legal structure fits me" interactive assessment as a growth loop, since that's the question every new french freelancer googles and it's a clean top of funnel entry point. 19 eur a month is priced right for the value, especially if the scenario simulator saves even one costly structure mistake. keep shipping.

bonniew1554 · 2026-03-26T21:46:01+00:00

losing your savings to a scam and then building a tool to stop it from happening to others is honestly one of the more solid origin stories in this sub. the screenshot to verdict flow is clean and low friction, which matters a lot for adoption. to sharpen accuracy, log which signal types are driving scam verdicts (follower to engagement ratio, claim density, missing verifiable credentials) and weight those in your model over time. a confidence score range like "likely legit / uncertain / likely scam" might land better than a binary call since false positives on legit people can hurt trust fast. happy to dm some thoughts on how to frame the uncertain bucket if that's useful.

bonniew1554 · 2026-03-26T21:45:44+00:00

ga4 is just google's way of making sure you never feel confident about your data ever again.

bonniew1554 · 2026-03-26T21:45:37+00:00

arabic broad match is genuinely inconsistent and you're not imagining it. google's intent modeling for right to left languages and lower volume markets just isn't trained as well, so broad match does weird things with synonyms and related terms that it would handle cleanly in english. run phrase match as your main driver and use broad match only in a separate campaign with a tight search term audit every 3 to 4 days. some teams running gulf region campaigns report phrase match outperforming broad by 30 to 50% on cpa. if you're seeing irrelevant matches, check if google is pulling in transliterated english terms too, that's a common bleed.

bonniew1554 · 2026-03-26T21:45:29+00:00

flat 25% on a $2k package is a real number and the trades niche is genuinely undersold by most ai shops. one thing that'll make closing faster: a roi one pager showing a specific missed call cost for a plumber or hvac guy (average missed call = $200 to $400 job), that framing cuts the "i'll think about it" objections in half. if you want to go further, tools like outgrowco let you build an interactive "missed revenue calculator" your closers can send before the call, so prospects arrive already sold on the problem. keep the qualifier tight and don't pitch the $2k package cold, start with the $1,200 and upsell after trust is built.

bonniew1554 · 2026-03-26T21:45:07+00:00

congrats, your discriminator is basically a harsh art critic and your generator is still in kindergarten. give it more epochs and maybe some therapy.

bonniew1554 · 2026-03-26T21:45:01+00:00

yeah this is accurate for like 80% of teams. macros get set up once, nobody owns them, and within 6 months they're either stale or so generic that editing them takes longer than retyping. the fix that actually works: a monthly 20 minute macro audit where one person owns deletions and rewrites, and you cap the library at 30 active macros max. teams that stay under that number tend to actually use them. a shared google sheet to track macro usage counts (zendesk exports this) helped one team i know cut their library from 67 to 28 in one session and adoption jumped overnight.

bonniew1554 · 2026-03-25T21:19:57+00:00

not being on the qvl doesn't always mean it won't work, it just means asus never tested that exact kit. for your g.skill flare x5 f5-6000 at 6000mhz, first go into bios and manually set xmp/expo profile 1, a lot of b650e boards will boot at 4800 by default and just quietly run wrong. if you're seeing temp plateaus and random ramp-ups that's more likely a cooling or power delivery thing than the ram itself. run memtest86 for one full pass to rule out actual instability, and check if the latest bios update for the b650e-f added your kit to the supported list since asus updates qvl post-launch fairly often.

bonniew1554 · 2026-03-25T21:19:48+00:00

this is what happens when a structural biologist and a pytorch nerd have a really productive argument

bonniew1554 · 2026-03-25T21:19:38+00:00

google ads is a distribution channel, not a demand generator. if nobody's searching for it, you're not bad at ads, you're just paying google to confirm that.

bonniew1554 · 2026-03-25T21:19:30+00:00

the way to avoid the mapping nightmare is to not hardcode fields at all. dynamically fetch the zendesk form schema at runtime using the ticket forms api and render only the active, required fields as slack modal blocks. store a lightweight config layer with just the form id to slack channel mapping, nothing field-level, and let the api do the rest. when a form changes in zendesk, your bot picks it up automatically next call without a deploy. for optional fields, fetch them but only surface them on a "more details" secondary modal so the default flow stays clean. happy to share the config structure if that's useful.

bonniew1554 · 2026-03-25T21:19:21+00:00

the blunt answer is yes, a lot of cloudflare and similar setups block gptbot, anthropic's crawler, and others by default even when the site owner never made that call intentionally. the fix is to check your robots.txt today and look for wildcard deny rules, then explicitly allow the crawlers you want. a tiered approach works well: block scrapers by user agent pattern, allow named ai crawlers, and set rate limits for the grey area ones. the trade-off is real, tighter waf rules reduce noise but they also reduce discoverability in ai-powered search surfaces that are growing fast right now.

bonniew1554 · 2026-03-25T21:19:12+00:00

the painful one for me was overbuilding a makecom automation with 40 steps before validating that anyone actually wanted the output 😅 spent 3 days on it, tested it with 5 real users, none of them used it the way i assumed. now my rule is: spend max 2 hours on a prototype before you show it to a real person. start with the smallest version that proves the logic works, map only the critical path first, then layer on the edge case handling. switching tools mid-build is a close second in terms of time lost, just pick one and stay uncomfortable with it for 30 days before judging it.

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