Particula Tech

Three founders called us last month burning $40K monthly on AI infrastructure. Zero revenue. Runway disappearing. Same question: "Is this normal?" No. It's a trap. They'd all assumed bigger models = better product. Built 70B parameter systems for tasks that needed 7B. We helped them rebuild. Same accuracy. 85% lower costs. Actual path to profitability. The AI industry has convinced startups that model size equals competitive advantage. It doesn't. Unit economics equal competitive advantage. The company that can scale profitably wins. Your AI strategy shouldn't be "biggest model we can access." It should be "smallest model that solves the problem." Capital efficiency isn't boring. It's survival.

A retail client came to us with an expensive problem. Their AI product recommendation system had stopped working. Not crashed - just stopped delivering results that made sense. The culprit? Their prompts had grown from 500 tokens to over 3,000 tokens. Here's what happened: They started simple. Basic instructions, a few examples, product data. It worked well. Then they added edge cases. More examples. Detailed formatting rules. Extra context "just in case." Each addition seemed logical. But performance collapsed. Response quality dropped. API costs tripled. During peak traffic, the system became too slow to use. They assumed the problem was their model. It wasn't. We tested their exact task at different prompt lengths. At 500 tokens, accuracy was 84% and responses took 1.2 seconds. At 3,000 tokens, accuracy dropped to 72% and responses took 5.8 seconds. The model wasn't getting better guidance from all that extra context. It was getting overwhelmed. We rebuilt their prompts at 1,100 tokens. We used semantic search to pull only the most relevant examples for each query instead of including everything. Results: 89% accuracy, 1.4-second responses, costs cut by 58%. Same functionality. Better performance. Lower cost. Most companies think more context always helps. It doesn't. There's an inflection point where adding more degrades performance instead of improving it. If your AI system isn't performing like you expected, check your prompt length first.

Three ways to know if reranking will actually improve your RAG system. (Most teams add it too early.) We reviewed several RAG projects last month. They integrated reranking because everyone said it would fix their accuracy problems. It didn't. Their retrieval was pulling decent chunks. But the chunk size was wrong. 512 tokens. Way too small for technical documentation. The reranker just reordered bad results. Three months integrating Cohere. Custom pipelines. Accuracy improved maybe 4%. Could've gotten 30% by fixing chunk size and overlap first. Here's when reranking actually helps: Your retrieval is solid but results need better ordering. You're already getting relevant documents in your top 20-50 results. Your embedding model is appropriate for your content type. When it doesn't help: You're not retrieving the right documents at all. Your chunk strategy is broken. Your metadata is messy or missing. Reranking amplifies what you're already doing. If you're retrieving mediocre results, you'll just get them in a slightly better order. Fix retrieval first. Test your chunk size. Clean your metadata. Then add reranking.

Here's how we decide between fine-tuning and RAG for clients. It's one of the first questions we get asked. And honestly, most companies are asking the wrong question. They want to know which technology is better. But the real question is: What problem are you actually solving? Fine-tuning changes how a model behaves. Use it when you need the AI to adopt a specific style, follow your methodology, or make decisions like your experts do. RAG feeds the model information it doesn't have. Use it when you need accurate answers from your own documents, databases, or knowledge base. Different tools. Different jobs. A SaaS company came to us last month asking us to optimize their fine-tuning setup. It was getting expensive and the results weren't great. We looked at what they were trying to do: answer questions about their product documentation. They didn't need fine-tuning at all. They just needed RAG. Switched them over. Costs dropped. Accuracy went up. Problem solved in three weeks. Before you commit to either approach, ask yourself: Are you teaching the model how to do something, or giving it what to know? That answer will tell you which direction to go. We wrote more about alternatives to RAG in our latest blog post → https://lnkd.in/eK5cFhZh

n8n is great for testing ideas. Terrible for production systems. We just told a client this. Here's why. The issues show up around month 3: Workflows that worked fine with 100 operations suddenly break at 1,000. Debugging becomes difficult because the visual interface hides what's actually happening. Making changes requires careful coordination because one workflow often depends on three others. The bigger problem: maintenance burden. When your team grows or processes change, those visual workflows become a tangled web. We've inherited three n8n implementations this year. Each time, rebuilding them properly took less time than untangling the existing setup. We typically recommend purpose-built solutions or custom code depending on the complexity. They cost more upfront but scale predictably and stay maintainable. n8n works well for specific scenarios - internal tools, simple integrations, rapid testing. Testing an idea or building an MVP? Sure, we'll use it. It gets you to validation fast. But for production systems handling customer data or revenue critical processes? That's where we draw the line. We write about these technical decisions regularly on our blog. Blog → https://lnkd.in/exVh9pyK The technical debt catches up fast.

We're looking for people to write about their actual AI implementation experience on our blog. Not theory. Not predictions. What you built, what broke, what you learned. Sebastian's been writing about our client work for a few months. Good response, good questions. But we're one perspective. We want to hear from others doing the real work: The integration that took 3 months instead of 3 weeks. The model that worked in testing but failed in production. The unexpected cost that doubled your budget. The workaround that saved the project. Format's flexible. 800-1500 words. Case study, technical breakdown, or honest post-mortem. We'll edit. Doesn't matter if you're at a startup, running an agency, or building side projects. If you've implemented something and have specific learnings, we're interested. Why: Most AI content is marketing. Companies are making expensive decisions based on generic advice from people selling something. We'd rather publish practical experience from people who've actually done the work. If you have something to share, like this post and send Sebastian a DM with your idea.