u/camerongreen95

Hey, please share link of authentic sellers ( expensive or cheap doesnt matter) who have quality just like of Goalthread and other sellers.

shipped an LLM feature, felt fine at launch, moved on to other work. a while later the bill showed up noticeably higher than expected and that was the first real signal anything had changed. no alert, no dashboard flag, just a number at the end of the month that made me go "wait, what happened."

went back and actually set up proper tracing and token/cost monitoring per request instead of just trusting it'd be fine, and found a specific workflow was making way more calls than i thought due to a retry loop that wasn't being logged anywhere visible. it had been quietly running up cost for weeks with zero visibility until the invoice.

also added latency budgets and caching for repeated queries after this, which cut cost noticeably on top of the retry fix.

feels like a pretty basic devops instinct (you monitor what you ship, you don't wait for the bill) that somehow gets skipped constantly once "AI feature" is involved, like people ship LLM stuff with less observability discipline than they'd ever accept for a normal service. anyone else's team caught something similar the hard way before actually building proper monitoring in from the start

so... for a while my process for deciding if a model change or prompt change actually helped was literally just "read a few outputs, does this feel better." which in hindsight is an insane way to make a decision that costs real money and affects real users.

started forcing myself to actually test this properly. built a small golden dataset, ran both versions against it, and instead of eyeballing, did a paired comparison with bootstrap confidence intervals to see if the difference was actually statistically meaningful or just noise from a handful of lucky/unlucky examples.

first time i did this properly i found out a change i was fairly convinced "felt better" actually wasn't statistically distinguishable from the baseline at all. i had just gotten a good sample of outputs by chance and convinced myself it was real. kind of embarrassing but also useful to know before shipping it as a confident upgrade.

feels like this space (LLM evals specifically) still runs on vibes way more than it should given how much rigor exists for exactly this kind of comparison problem already. anyone else doing paired significance testing on model/prompt changes or is this not common practice yet where you are

kept building RAG projects the same lazy way for a while, get it running, read a handful of outputs, they look reasonable, ship it. no real measurement, just vibes.

finally forced myself to sit down and build an actual evaluation set. nothing fancy, about 40 question and answer pairs where i already knew, in advance, exactly which document had the correct answer. then ran the pipeline against all 40 and checked, for each one, did the retrieval step actually surface the right document in its results at all.

immediately found problems the "looks fine" outputs had completely hidden from me. retrieval was missing the correct document entirely on something like 1 in 5 questions, which is a genuinely bad number, way worse than the vibe i had from reading a handful of good-looking answers. the outputs that did come back for those failing cases still sounded confident and reasonable, they just weren't grounded in the right source at all, which is exactly why eyeballing outputs never caught it.

the annoying part is building the eval set felt like the boring, unglamorous step compared to actually tuning chunking or trying a new embedding model. but it ended up being the single most useful thing i did on the whole project, since without it i had literally no way to know if any change i made afterward was helping or hurting. tuning without measurement is just guessing with extra steps.

anyone else find the eval work more valuable than expected once you actually forced yourself to sit down and do it properly, instead of skipping straight to the fun part of tuning things?

so i was all in on vector-only retrieval for a while. cosine similarity, top-k, done, ship it. worked totally fine on easy conversational stuff, the kind of queries that show up in every demo.

then real usage started and it fell apart on anything where exact wording actually mattered. product codes, specific numbers, exact names, anything where semantic similarity works against you because two completely different things can "feel" close in embedding space. asked about invoice #4471 and got back chunks about invoices in general, close in vector space, useless in practice.

took embarrassingly long to admit the fix was going backward, adding keyword search back in alongside vector (BM25 style) and fusing both result sets with reciprocal rank fusion. felt like a step back honestly, going back to keyword matching in 2026 when everyone's talking pure embeddings. but it caught a big chunk of the exact-match failures vector alone was quietly eating.

what surprised me more was how much reranking on top of that mattered too. initial retrieval (vector + keyword combined) gets you a decent candidate set, but a second pass that actually scores relevance against the full query, not just similarity, caught cases where the right chunk was in the top 20 but never made the final top 5 that actually got used.

so now it's basically a 3 stage thing: hybrid retrieval first, rerank second, then whatever's left goes to the model. feels like more moving parts than i wanted, but the accuracy jump was real, not marginal.

curious how many people here are still running pure vector-only vs doing hybrid by default. genuinely feels like hybrid should be the baseline at this point, not the advanced option, but i still see a lot of vector-only setups in the wild

so this one bugged me for a while... had a case where the answer was 100% somewhere in my corpus, retriever pulls back something plausible, model answers confident, still wrong.

turns out it's not really a generation problem, it's retrieval failing in ways plain cosine similarity just... can't fix. two patterns specifically:

multi-hop stuff. like asking "who are company X's indirect suppliers." one doc says firm A supplies firm B, another says firm B supplies firm C, but no single chunk has the full chain. similarity search has zero concept of A → B → C, doesn't matter how good your reranker is, it's just not there to find.

global questions. "what are the main themes across these 500 docs" type stuff. top-k retrieval grabs like 10 chunks closest to the query and just... ignores the other 10k. which makes sense actually, that's a summarization job, not a retrieval job, but everyone throws it at their retriever anyway and wonders why it's bad at it.

the thing that clicked for me was realizing the model isn't "hallucinating" out of nowhere in these cases, it's inventing connections exactly where retrieval failed to hand it real structure. the docs had the answer the whole time, my pipeline just wasn't built to find it.

anyone else dealt with the multi-hop thing specifically? curious what people are actually doing about it besides just cranking up k and hoping

Spent weeks trying different embedding models trying to fix retrieval quality. What actually moved the number was adding a re-ranking pass, a second retrieval stage that re-scores the top results using a model that actually considers full query context, not just similarity.

Basic vector search alone misses this entirely, it grabs what's similar, not necessarily what's most relevant to the actual question being asked. Adding a cross-encoder reranker on top of the initial retrieval step caught a surprising number of cases where the right document existed in my corpus but wasn't making it into the final context window.

Anyone else found re-ranking underrated compared to how much attention embedding choice gets?

Looking for sellers in delhi / delhi ncr region. I need large number of customized jersey for me and my team.

Will prefer meeting f2f for first time

I kept hitting a specific failure pattern that took a while to actually understand. The answer exists somewhere in the document set. Retrieval pulls back something plausible. The model answers confidently. Still wrong.

I realized eventually it's almost never a generation problem, it's retrieval failing in ways vector similarity structurally can't catch. Three patterns specifically:

Multi-hop questions. Something like "who are Company X's indirect suppliers," where the chain lives across separate documents that never reference each other directly. Vector similarity has no concept of connecting A to B to C, no amount of reranking fixes that.

Global questions. "What are the main themes across these 500 docs" is a summarization task, not a retrieval task. Top-k retrieval grabs a handful of chunks by design and ignores the rest.

Explainability. A chunk scoring 0.87 on similarity tells you it's relevant. It doesn't tell you why the model landed on its final answer, which matters once anyone outside engineering is reviewing the system.

What actually helped was stepping back and treating the whole pipeline as an engineering problem, not an AI problem. Chunk size and overlap need actual testing, not guessing. Metadata on every chunk, source, section, date, is what makes debugging possible later. And without an evaluation set, you won't catch retrieval quality regressions until a user does.

Curious what failure patterns others here have hit that don't fit into these three.

I kept hitting a specific failure pattern that took a while to actually understand. The answer exists somewhere in the document set. Retrieval pulls back something plausible. The model answers confidently. Still wrong.

I realized eventually it's almost never a generation problem, it's retrieval failing in ways vector similarity structurally can't catch. Three patterns specifically:

Multi-hop questions. Something like "who are Company X's indirect suppliers," where the chain lives across separate documents that never reference each other directly. Vector similarity has no concept of connecting A to B to C, no amount of reranking fixes that.

Global questions. "What are the main themes across these 500 docs" is a summarization task, not a retrieval task. Top-k retrieval grabs a handful of chunks by design and ignores the rest.

Explainability. A chunk scoring 0.87 on similarity tells you it's relevant. It doesn't tell you why the model landed on its final answer, which matters once anyone outside engineering is reviewing the system.

What actually helped was stepping back and treating the whole pipeline as an engineering problem, not an AI problem. Chunk size and overlap need actual testing, not guessing. Metadata on every chunk, source, section, date, is what makes debugging possible later. And without an evaluation set, you won't catch retrieval quality regressions until a user does.

Curious what failure patterns others here have hit that don't fit into these three.

(Sharing this because its seems relatable for this subreddit..)

Anyone else has faced this?...Everything works great in testing, then real data comes in, messy docs, weird phrasing, edge cases you didn't think to test, and suddenly it's answering wrong with total confidence. Took me a minute to realize it usually isn't the model's fault at all.

Two things kept tripping me up specifically. First, questions where the answer isn't in one place, like asking who a company's indirect suppliers are, when the chain is spread across three different documents that never mention each other directly. Vector similarity just can't connect dots like that, doesn't matter how good your reranking is.

Second, anything that needs the whole picture, "what are the themes across all these docs" type questions. Retrieval just grabs the top handful of chunks and ignores the rest, so you're basically asking a search tool to do a summarizing job it was never built for.

If you're deep in this stuff, there's a workshop on Aug 8 that actually digs into fixing this, retrieval tuning, evaluation, governance, all the stuff that separates "works in the demo" from "actually holds up." I am joining.

Dropping the link in comments if anyone else wanna join as well.

Hey guys, i am looking for people who use Reddit for marketing. I am making a whatsapp/(any other platform) group where we can help each other grow on reddit.

Please dm or comment if you want participation

Spent a while debugging an agent that kept giving confidently wrong answers when it pulled from a knowledge base. assumed it was a reasoning problem, tried different prompts, tried a bigger model, nothing fixed it consistently.

turned out the agent was reasoning fine. it just had no way to know the chunks it retrieved were incomplete or irrelevant. the actual problem was upstream.

What was actually broken:

- chunking strategy was splitting context in the wrong places

- no metadata attached at ingestion time to filter by source or recency

- no evaluation step to catch bad retrieval before it reached the agent

Why this matters more for agents specifically

An llm on its own will usually flag uncertainty. an agent chaining tool calls on top of bad retrieval just keeps building on a broken foundation. wrong information gets treated as ground truth for the next step, and the failure compounds instead of showing up as an obvious error.

Four things worth checking if your agent's retrieval keeps failing:

- are chunks getting split mid context, losing the information that would have answered the query

- is there any metadata attached at ingestion time to filter or rank results

- is there an actual evaluation set, or is quality judged by spot checking outputs

- is the agent given any way to say it doesn't have enough context, or does it always generate an answer regardless

There's a hands on workshop on aug 1 that goes through building this properly, ingestion, chunking, embeddings, retrieval, and evaluation with real metrics.

happy to share details in the comments if useful