AI is everywhere, and conventional wisdom says you’re falling behind if you’re not using it. Naturally, many flock to ChatGPT, impressed by its vast knowledge and comprehension—and occasionally chuckling at its hallucinations. As a software developer or organization building applications, what’s your AI strategy? The conventional path seems clear: start with ChatGPT, move on to frameworks like LangChain for Retrieval-Augmented Generation (RAG), and eventually develop sophisticated pipelines and agentic AI. But here’s the contradiction: the right approach for software developers is to focus on fine-tuning to improve the models themselves. RAG works by indexing external knowledge into vector databases and appending retrieved snippets to prompts—essentially giving the LLM a cheat sheet for each response. This method has fundamental limitations: LLMs don’t truly learn from these snippets; they only reference them temporarily. The result? Inconsistent outputs and unnecessary system complexity. The LLM remains one step removed from this corpus of knowledge. If you have specialized data or domain expertise, why settle for this halfway solution? Instead of patching a base LLM with external retrieval, fine-tune the model directly on your data. Fine-tuning weaves your knowledge into the model’s parameters, creating a smarter, more reliable solution tailored to your specific needs. Yes, RAG is popular because it’s quick to implement and avoids costly retraining. But it’s a temporary fix, not a lasting solution. Fine-tuning is the true path to AI that deeply understands your domain, delivers consistent results, and scales effectively. Best of all, it’s now accessible even to smaller players without billion-dollar budgets. In short: Don’t fall for the trend of layering retrieval on top of base models. If you want AI that truly serves your users, make fine-tuning your primary strategy—not just an afterthought. What’s your perspective? Are you still relying on RAG, or have you embraced fine-tuning as the way forward?
Why fine-tuning is better than RAG for AI
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attempted at doing some sort of pvp (permissionless value prop) using claude code and chatgpt (twice), failed horribly. but some things i've learned from actually iterating throughout the process: 1. choosing your ai model for the task chatgpt extended thinking > claude thinking - much faster processing times and gpt used its context window much better than claude. claude just ingested way too much data and broke after two prompts. 2. ai data manipulation + caveats ai is REALLY good at manipulating data as long as what you tell it to do doesn't require multiple layers which it can't understand. for example, if you tell ai to "write copy based on this data," it can understand the first layer of how to write copy, but it doesn't understand the other layer, human psychology. 3. start with the customer first way too quickly, i found myself diving into this rabbit-hole of this particular dataset only to find that this dataset doesn't even make sense to reach out to the person/company for. for example, i had a dataset that i could transcribe and then send home service businesses in this area a heatmap of all of the zip codes that were looking for their services. but then digging deeper, i found out that this company's icp is actually b2c home service providers instead of b2b. 30m wasted. go slow in the beginning and then ramp as you know this is the vertical you want to push into. anyways, going to be iterating throughout the process over the rest of the weekend. thought i'd share for anyone trying to learn how to run pvp effectively + efficiently
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The Secret to Making LLMs Smarter (It's Not More Data!) We all know Large Language Models (LLMs) like ChatGPT are amazing, but they have one big limitation: their knowledge is static. They can't tell you the weather right now or the live stock price because they're cut off from the real world. But there's a powerful solution that fixes this problem: Function Calling (or Tool Use). What is Function Calling? Think of it as giving an LLM access to the internet through specific apps Here’s how it works: You Ask: “What’s the weather in Bengaluru ?” The LLM Realizes: It needs live, external info. It Calls: A weather API to fetch the current data. It Answers: “It’s 24°C in Bengaluru right now.” (Accurate and real-time!) Why This Matters for Business: Function Calling instantly connects your LLM to any external service your company's database, a customer support system, or a financial tool. This capability transforms an LLM from a static knowledge base into a powerful interactive agent that can: Process real-time transactions. Get live updates. Perform actions in the real world. If you're building with AI, understanding how to integrate APIs through Function Calling is the key to creating truly useful, dynamic applications. Siddhant Goswami Day 44 of my #0to100xEngineers journey. #0to100xEngineer #100xEngineers #GenAI #AI #LLM #FunctionCalling #ToolUse
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Did you know LLMs have no memory? In tech terms, they are stateless. They have literally no memory from one prompt to the next. The apps that you use like Copilot or ChatGPT or Claude or DeepSeek are performing the magic of feeding your prior prompts (or more likely a summary of them) into the LLM so it appears the LLM remembers what you were talking about. Without this simulated memory, these apps would lose a lot of their utility. This is not a fun bit of tech trivia. This is a very important distinction from what you probably thought was happening. And it gets to the heart of one of the most fundamental aspects of AI system design. What you feed into your LLM in the form of a prompt will have a profound impact on what you get out of your LLM. This is important for the chat applications you use daily, but it is vital for specific intelligence you will be feeding your LLM. If you want to elevate your LLM from a generalist to a specialist in your problem domain, you are going to need to feed it very relevant details on the exact prompt from the user. The more I talk to AI app users, the clearer it is that these details are obscure. This obscurity can make understanding and building a great AI app very difficult. If you think you are going to train your own AI model, you are going to be very surprised by the difficulty (not to mention the cost!). If instead you understand that the secret is great prompt "injection," you will be able to build a very inexpensive and efficient tool that can do what you need! So before you start, understand what you are really going to be doing. You will be injecting a combination of past memory, relevant facts, and the user prompt into a template that you will hand off to your LLM. Tweaking and refining this process is straightforward and fairly logical. Odds are you can get very close to you goal with this simple recipe. I want to dig into memory storage and fact storage as well. They are pretty simple concepts--and understanding them will give you a great roadmap to success with your AI system development!
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Unpopular opinion: "AI-powered" is the new "blockchain-enabled." It means nothing. I reviewed 30 AI SaaS websites this week. They all say the same thing: "AI-powered analytics" "Intelligent automation" "Next-gen AI platform" "Machine learning insights" Your prospects are asking: "Is this just ChatGPT with a wrapper?" 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: Stop leading with the technology. Nobody cares about GPT-4 or LLaMA. They care about outcomes. What can they do 10 times faster? Start with specific outcomes for specific people. Not "AI-powered analytics for teams" but "See which features customers actually use in 30 seconds." Show the AI magic in action. A 30-second demo beats an "AI-powered" badge every time. Address the elephant in the room. Add a section: "Why not just use ChatGPT?" and answer it honestly on your homepage. Make AI invisible and outcomes visible. The best AI products don't talk about AI. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Jasper doesn't say "AI writing assistant powered by large language models." They say "AI content that sounds like you." Mem doesn't say "AI-powered note-taking with vector embeddings." They say "Your self-organizing workspace." Harvey doesn't say "GPT-4 integration for legal professionals." They say "AI for elite law firms." 𝗦𝗲𝗲 𝘁𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻? Your prospects don't wake up thinking "I need AI-powered software." They wake up thinking "I need to stop losing deals to competitors." If your AI solves that, lead with THAT. Not the fact that you use AI. Because in 2025, everyone uses AI. It's table stakes. The companies that win will make prospects say "This is exactly what I need" instead of "Another AI tool." What's the worst "AI-powered" positioning you've seen? Drop examples in the comments.
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ChatGPT, Gemini, and Grok are all trained on publicly available data. But here’s what happens when you train AI on your own data. You get a Retrieval Augmented Generation (RAG) Agent. This kind of AI doesn’t just know things; it understands your world. You can feed it countless books, articles, YouTube videos, research papers, and even terabytes of data on Quantum Mechanics. It can then answer hyper-specific questions, solve complex problems, and go in-depth in ways that even ChatGPT can’t. A RAG Agent can be trained on any type of information and can help solve problems or clear doubts based entirely on the data it has been trained on. This becomes extremely powerful for coaches and consultants who can’t be available for their clients 24/7 but still want to provide accurate, high-quality feedback and advice. Here’s how I built one in just 60 minutes, in the simplest way possible. I used n8n to create the entire setup. First, I collected all the information I wanted to feed the RAG Agent. In this case, it was a document titled “Content Strategy for Authority Building.” Then, I built a small automation to retrieve that document from Google Drive (you can use any cloud storage) and upload it to Pinecone. Pinecone is a vector database that allows the AI to access huge amounts of data and extract the most relevant information at lightning speed. Once that was ready, I moved on to building the actual agent. I used a chat trigger in the workflow to enable real-time conversations with the agent. Then I connected it with three key components: • A memory that helps the agent remember the ongoing conversation and context. • A vector store tool that allows the agent to access and use data from Pinecone effectively. • An LLM (Large Language Model), which acts as the brain. In this case, I used Gemini. After the setup was complete, the RAG Agent was ready to go. Now I can ask it any question based on the data it has been trained on. The more detailed and high-quality the information I feed it, the deeper and more accurate its responses become.
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It's hard to build a CHEAP chatbot on botpress But I've cracked the code It took me months of testing and building to learn these so a lil thumbs up 👍 wouldn't hurt :) By CHEAP I'm referring to the AI spend of your bot Here's the trick to spend as little as possible: 1. Pick the RIGHT LLM Each LLM has different costs, speeds, and smarts Pick a cheap one = you get cheap responses Pick GPT5 = you get a heavy monthly bill The trick? Balance Quality isn't just GPT ᐅ Try Cerebras ᐅ Try Meta's Llama ᐅ Try Anthropic's Haiku ᐅ Try Deepseek Test them all until you find what works 2. Turn off auto conversation summaries It's an agent that burns AI credits every convo If you're not using them just turn them off. (You'll save way more than you think) 3. Use AI only when necessary Doing a calculation? Don't use AI Use an Execute Code card with JavaScript instead. "But Enoch, I don't know code!" Cool. Ask ChatGPT to write it for you. Then paste it in. Bro it's 2025. "I can't code" is no longer an excuse. Stop making Sam Altman richer for simple tasks These 3 tips save me money every month Great for me Great for my clients ___________ Enoch Business owner? Click my profile and find the special gold ticket (hint: banner...)
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Last week I shared a service that converts SQL to PySpark. Someone asked: "What does this do that ChatGPT can't?" Fair question. And honestly? Nothing fundamentally different. Under the hood, it's probably using the same LLM API that ChatGPT uses. But the value isn't in the AI itself. It's in how you apply it to solve a specific problem: → Narrow use case → Clean UI → Perfect system prompts → Additional context that makes responses more accurate There's a whole industry called "AI wrappers," and there's nothing wrong with that. They take general AI capabilities and make them useful for specific problems. So yes, you could use ChatGPT for everything. But sometimes a focused tool saves time, reduces friction, and just works better for what you need. I like Daniel Priestley's analogy: 𝗔𝗜 𝗶𝘀 𝗹𝗶𝗸𝗲 𝗲𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗶𝘁𝘆. You can use it to light your house, heat your home, or power your microwave. Same source, different applications. #AI #AITools
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Was doing PEFT creating LoRA adapters over 1.5 years ago. Thinking it was going to be the way. Now with context windows being so big, we find just using deterministic code to load in just the knowledge needed to ground the model does great. RAG is best for “impromptu” use cases but most needs not required. But I do agree if you want less probabilistic outcomes, FT helps you scratch out more consistency and accuracy.