As professionals, we've been bombarded by tales of intelligent AI systems that can do it all. We've been warned that if we don't jump on the train we will be left behind and our jobs/business will disappear, leaving us with nothing but regret for not being quick or "smart" enough to take advantage of this trending technology.
But most of the time, when we try to use these tools, the results are very often underwhelming... Our ChatGPT chats promise that it performed a deep market research and that it found for us the next big idea for our business, just to realize that the next big idea according to it, is either to start a company to compete with Google or Microsoft, or to invent a new revolutionary product that enables humans to go from one point to another without needing any sort of engine, only a chain, pedals, and two wheels... But one Google search query later we realize that we are probably too small of a business to compete with any hyperscaler or any well funded start-up for that matter, and that bicycles already exist!
All jokes aside, it's indeed not very prudent to ask AI chatbots to perform market research for us. And with the constant advancement of the technology and each new release, these tools become more capable at keeping up-to-date and suggesting recent information. But the reality remains that tools such as ChatGPT, Gemini or Claude, due to their architecture and training processes, will always provide you with wrong input sometimes. And it's up to you to catch that and put it back on the path you desire.
In fact, in this blog we will tell you all about artificial intelligence, starting from definitions to free tools and simplified techniques created specifically to integrate into the workflows of non-technical users. In order to achieve that, we've spent months and important sums of money on buying equipment and on electricity. The aim was to discover this world beyond the hype of the online tools and marketing materials.
We've tested closed-source models, and spent hours trying to make open-source ones perform at their best. In this post and the upcoming ones on our blog, we will synthesize and simplify what we discovered, so you don't have to go through the same painful (but fun :D) journey as us.
Artificial Intelligence Definition
We won't start from the beginning of AI technology--this is after all not a YouTube channel about history--but if you are interested in learning about the origins of the term and the technology, you can visit Wikipedia.
The best definition of artificial intelligence nowadays (we are referring to Large Language Models here), is that they are advanced statistical models capable of predicting, with high accuracy, the next words in a sequence of words. This capability makes them able to generate text based on prompts, write code, or even trigger the usage of certain tools. But under the hood, all of these tasks are accomplished by asking "what is the next logical text/code/tool that fits in the sequence of the user's prompt?"
AI models come in different architectures and formats. Most of us have probably already interacted extensively with two forms of these models:
- Don't use free instances of closed-source models for important and sensitive tasks.
- Don't use open-source models online (unless you host them yourself).
- The best model for you might be an open-source one, but in that case you will need to host it yourself.
- Bigger models are generally better, but small specialized ones perform better on their specific tasks.
Hallucination: When Models Try to Please
While capable of performing online search, your AI chats are limited to a knowledge base that preceded their cut-off date. Meaning that, by default, they will always try to answer your prompt with what they know already, which means the response might be outdated, or simply wrong (because what you are asking about didn't exist when they were trained).
Due to their architecture, these models will always give you answers even if they simply don't have any. This is called hallucination, and it's when AI invents factual information. Hallucination can also come in the format of skewed information--this means that the model changes information you've already provided, either because it didn't understand the nuances or it simply forgot the context.
- Make sure you draft a clear prompt that follows the recommendation of each provider.
- Always perform your own online research and compare facts.
- Ask AI for references and to expose its reasoning. Make sure the reasoning is correct and the references actually exist.
- Challenge any output you suspect of being wrong.
- Make sure the model didn't switch to a different approach than the one you requested--it does that silently.
- Keep your prompts short and specific to a narrow task to avoid hallucinations.
- Provide the model with online documentation and references and ask it to analyze them carefully and provide answers on their basis.
- When you attach a document to your prompt, make sure you mention it explicitly so the model actually opens it.
- Ask the model to acknowledge lack of information and not to try to fill in the gap with made-up facts.
Context Issues: When Models Lose Track
What made models such as ChatGPT and Claude possible is the ability to make machines "pay attention" to the current task without losing sight of the overall objective. This mechanism is called Attention, and it's great... when it works.
Transformers don't really focus in a human sense of the word. They simply have a context window that, as long as it still fits information, the model will perform well. But once this context is full, let's say it will then start a new session with some context from the previous one, but not all. This causes huge drops in performance on important/long-running tasks.
- Segment your tasks into small ones.
- Optimize your prompts to focus on one task at a time and direct the chain of thought of the model.
- Ask the model to create a to-do list and to update it as work progresses.
- Ask the model to keep context and checkpoint files and to update them regularly.
- Use sub-agents, when possible, to perform sub-tasks.
- Use RAG to ground the model into your domain-specific knowledge.
Prompting: The Art of Manipulating Your Model's Outputs
Here's the thing that took us way too long to understand: the quality of what you get from AI depends almost entirely on how you ask for it. We're not talking about being polite (the model doesn't care if you say "please"), we're talking about structure, clarity, and knowing what actually triggers good responses.
After hundreds of hours testing prompts across different models, we've boiled it down to what actually works versus what's just noise in the prompt engineering echo chamber.
The Anatomy of a Prompt That Actually Works
Forget the complicated frameworks with acronyms. A good prompt has three things: context, task, and constraints. That's it.
Context tells the model who it's supposed to be and what situation it's dealing with. "You are a senior financial analyst reviewing quarterly reports" works better than just asking for financial analysis. Why? Because the model draws from different patterns depending on the role you assign.
Task is what you actually want. Be specific. "Summarize this document" is vague. "Extract the three main risks mentioned in this document and explain each in two sentences" gives the model something concrete to deliver.
Constraints are the guardrails. Length limits, format requirements, what to include, what to avoid. Without these, you get the model's default output, which is usually too long, too generic, and formatted however it feels like.
Techniques That Make a Real Difference
Few-shot prompting means showing examples before asking for output. If you want a specific format, don't describe it--show it. Paste an example of what a good output looks like, then say "now do the same for this." Works every time.
Chain of thought is asking the model to reason step by step. For anything involving logic, math, or multi-step analysis, adding "think through this step by step" or "explain your reasoning" dramatically improves accuracy. The model catches its own errors when forced to show its work.
Role assignment changes the vocabulary and depth of responses. "As a lawyer specializing in GDPR" produces different output than "as someone who knows about privacy laws." Be specific about expertise level.
Negative instructions matter more than you'd think. "Do not include generic advice" or "avoid marketing language" cuts out the filler that makes AI outputs feel artificial. Tell the model what NOT to do.
What Doesn't Work (Despite What You Read Online)
Adding "please" and "thank you" does nothing. Being extremely verbose in your instructions often confuses more than it helps. Threatening the model ("this is very important, don't mess it up") is useless. And those super-long prompt templates with 15 different sections? Usually overkill for most tasks.
Where to Find Quality Prompts
GitHub Awesome Lists - Search for "awesome-claude-prompts" or "awesome-anthropic." These curated lists collect high-quality prompts organized by use case. Community-vetted means they've been tested by multiple users.
Anthropic's Official Cookbook - The Anthropic Cookbook contains official examples and best practices. This is the source of truth for how Claude is designed to work.
Prompt Engineering Communities - Reddit's r/ClaudeAI and various Discord servers share tested prompts. Quality varies, but the good ones get upvoted.
LangChain Hub - While focused on development, LangChain's prompt hub contains Claude-optimized templates that you can adapt for direct use.
- Start simple, then add complexity only if needed.
- If the output isn't right, iterate. Ask the model to fix specific problems rather than rewriting your entire prompt.
- Save prompts that work. Build a personal library for recurring tasks.
- Different models respond differently to the same prompt. What works perfectly on Claude might need adjustment for GPT-4.
- Test your important prompts multiple times--outputs vary, and you want to know the range.
RAG: Retrieval-Augmented Generation
RAG is probably the most important concept if you want AI to actually be useful for your specific business or domain. And honestly, it's not as complicated as the technical articles make it sound.
Here's the problem RAG solves: AI models know a lot about the world in general, but they know nothing about your company, your products, your internal processes, or that spreadsheet you've been updating for three years. When you ask ChatGPT about your specific situation, it guesses based on general patterns. RAG lets you give the model your actual documents to work with.
How RAG Actually Works
Think of it like an open-book exam versus a closed-book exam. Without RAG, the model takes the test from memory alone (its training data). With RAG, it gets to look up information in a reference book (your documents) before answering.
The technical process has three steps:
Step 1: Preparation. Your documents get chopped into smaller pieces (chunks) and converted into numerical representations (embeddings). These embeddings capture the meaning of each chunk. Everything gets stored in a special database that can search by meaning, not just keywords.
Step 2: Retrieval. When you ask a question, that question also gets converted to an embedding. The system finds the chunks most similar in meaning to your question. Not keyword matching--actual semantic similarity. Ask about "reducing customer churn" and it might find content about "improving retention rates."
Step 3: Generation. The relevant chunks get inserted into the prompt along with your question. Now the model generates an answer based on your actual documents, not just its general training.
Why This Matters for Your Business
Without RAG, you're limited to what the model already knows. With RAG:
- Your customer support bot can actually answer questions about YOUR products
- Your internal assistant knows your company policies
- Your analysis tools work with your actual data
- The model stops hallucinating facts about your specific domain
The difference is dramatic. We've seen accuracy on domain-specific questions go from 40% to over 90% just by implementing basic RAG.
The Catch (There's Always a Catch)
RAG isn't magic. The quality of retrieval determines everything. If the system pulls irrelevant chunks, the model generates garbage. Chunking strategy matters--too small and you lose context, too big and you dilute relevance. And you need to keep your document database updated, or you're back to working with stale information.
- Start with a small, high-quality document set rather than dumping everything in.
- Test retrieval quality separately from generation quality. Make sure the right chunks are being found.
- Overlap your chunks slightly so information that spans boundaries doesn't get lost.
- Consider hybrid search (combining keyword and semantic) for technical content where exact terms matter.
- RAG doesn't replace good prompting--you still need clear instructions for the generation step.
Training and Fine-Tuning
We need to clear up a common confusion: most of us will never train an AI model from scratch. That requires millions of dollars and massive computing infrastructure. What's actually accessible to normal humans and businesses is fine-tuning, and even that might be overkill for your needs.
The Difference Actually Matters
Training means building a model from zero. You start with random weights and feed it enormous amounts of text until it learns language patterns. This is what OpenAI did to create GPT-4, what Anthropic did for Claude. We're talking about billions of dollars, months of compute time, and teams of PhD researchers.
Fine-tuning means taking a pre-trained model and adapting it slightly for your specific use case. You're adjusting an existing model, not building one. This is accessible to businesses and sometimes even individuals.
Prompt engineering and RAG (what we covered above) require no model modification at all. You're working with the model as-is but providing better instructions and information.
When Fine-Tuning Actually Makes Sense
Fine-tuning is worth considering when:
- You need the model to consistently follow a very specific format or style
- You have thousands of examples of the exact input-output pairs you want
- RAG and prompting aren't getting you close enough to desired behavior
- You're willing to manage a custom model deployment
Fine-tuning is probably NOT worth it when:
- You just want better answers (try better prompts first)
- Your use case changes frequently (fine-tuned models are static)
- You don't have high-quality training data
- You can achieve your goals with RAG
The Practical Options
Full fine-tuning adjusts all model parameters. Expensive, requires significant compute, gives you the most control.
LoRA and QLoRA (Low-Rank Adaptation) adjust only a small subset of parameters. Much cheaper, nearly as effective for most use cases, and you can even do it on consumer GPUs in some cases. This is what most businesses actually use.
Instruction tuning specifically trains the model to better follow instructions in your format. Good for consistent output structure.
RLHF (Reinforcement Learning from Human Feedback) uses human ratings to improve outputs. This is how the big labs make models "helpful"--but it requires substantial human labeling effort.
Our Honest Recommendation
We've spent significant time and money experimenting with fine-tuning. Our conclusion? For 90% of business use cases, well-designed prompts plus RAG get you there without the complexity of maintaining custom models. Fine-tuning is a tool, but it's not the first tool you should reach for.
- Exhaust prompting and RAG options before considering fine-tuning.
- If you do fine-tune, start with LoRA--it's cheaper and works well.
- Quality of training data matters more than quantity. 500 excellent examples beat 5,000 mediocre ones.
- Fine-tuned models need retraining when you want to change behavior. Factor in ongoing maintenance.
- Test extensively before deploying. Fine-tuning can introduce unexpected behaviors.
Skills & Modes: The Case of Claude AI
Anthropic's Claude has introduced something that changes how we think about AI assistants: the concept of skills and extended thinking modes. If you've been using Claude and noticed it sometimes performs dramatically better on certain tasks, this is why.
What Are Claude Skills?
Skills are essentially specialized instruction sets that Claude can access for specific types of tasks. Think of them as expert modes. When you ask Claude to create a presentation, it can access a skill that knows best practices for PPTX creation. When you ask for a spreadsheet, there's a skill for that too.
The practical impact? Claude doesn't just generate text anymore--it generates outputs optimized for the actual format you need. We've tested this extensively, and the difference in quality between generic outputs and skill-enhanced outputs is significant.
Anthropic maintains an official repository at github.com/anthropics/skills containing production-grade skills for document generation. The docx, xlsx, pptx, and pdf skills come pre-included with paid Claude plans.
For community-created skills, ComposioHQ/awesome-claude-skills aggregates 60-80+ skills across 11 categories. This is the closest thing to an app store for Claude capabilities.
Extended Thinking Mode
This is Claude's chain-of-thought on steroids. When enabled, Claude takes more time to reason through complex problems before responding. You can actually see it working through the logic step by step.
For simple questions, this is overkill. For complex analysis, code debugging, or multi-step reasoning? It's a game-changer. We've seen accuracy improvements of 30-40% on logic-heavy tasks just by enabling extended thinking.
Computer Use and Artifacts
Claude can now interact with tools and create actual files, not just text. Need a document? It creates a real .docx. Need a spreadsheet? Actual .xlsx. This moves Claude from "AI that writes about things" to "AI that makes things."
- Don't use skills blindly--understand what they're doing and why.
- Combine skills when tasks require multiple capabilities.
- Community skills are starting points, not finished products. Adapt them.
- Share back when you improve something. The community benefits.
- Watch for outdated skills--Claude updates change what works best.
Claude Code: The Terminal-Based Power Tool
Here's where things get interesting for developers. Claude Code is Anthropic's terminal-based agentic coding assistant, and it's fundamentally different from inline autocomplete tools like Copilot.
What makes it different? It runs locally in your terminal without a backend server, maintains massive context across your entire codebase, makes coordinated multi-file changes, and executes autonomously with a permission system. The extended thinking system (think < think hard < ultrathink) allocates more compute budget for complex problems--yes, those are the actual commands.
Installation is dead simple: curl -fsSL https://claude.ai/install.sh | bash on Mac/Linux. You'll need a Claude subscription (Pro at $17/month or Max at $100-200/month) or API access.
Here's a stat that surprised us: over 90% of Anthropic's own engineers use Claude Code for git operations--commit messages, complex rebases, history searching. The /init command generates a CLAUDE.md file that gives Claude persistent context about your project's patterns and conventions.
Quick Decision Guide
| What You Need | Start Here |
|---|---|
| Official document skills (Excel, Word, PDF) | anthropics/skills--already included with paid Claude |
| Pre-built workflow skills | awesome-claude-skills--browse by category |
| Daily coding with full codebase context | Claude Code CLI--the foundation |
| Building complete features systematically | get-shit-done--structure without enterprise overhead |
| Building custom skills | anthropics/skills template-skill + skill-creator |
Trends to Watch in 2026
We've spent enough time in the trenches to have opinions about what's coming next. Not the science fiction stuff--the practical shifts that will change how you actually use these tools in the next 12 months.
Agentic Workflows: Small Specialized Models Working in Tandem
The "one giant model to rule them all" era is ending. What's emerging instead is orchestration--multiple smaller, specialized models working together on complex tasks, each handling what it does best.
Here's why this matters: a 7B parameter model fine-tuned specifically for code review can outperform GPT-4 on that narrow task while costing a fraction to run. String together a code reviewer, a security scanner, a documentation writer, and a test generator, and you have a pipeline that beats any single general-purpose model.
We're already seeing this in production. Companies run cheap, fast models for initial classification, then route complex queries to more expensive models only when needed. The cost savings are dramatic--we've seen 60-80% reductions in API bills without quality loss.
AI Orchestrating AI: Agent-to-Agent Communication
This one's harder to wrap your head around, but it's happening fast: AI systems that manage other AI systems.
AI as judges is already mainstream. When you need to evaluate thousands of AI outputs, you can't have humans review each one. So you use a separate AI to score quality, flag problems, and filter results. OpenAI, Anthropic, and Google all use this internally. The technique is called "LLM-as-a-judge" and it's becoming standard for any serious AI deployment.
Agent-to-agent protocols are the next frontier. Anthropic's MCP (Model Context Protocol) and Google's A2A (Agent-to-Agent) protocol are competing standards for how AI agents communicate with each other and with external tools.
What This Means for You
The professionals who thrive with AI in 2026 won't be the ones who master one tool. They'll be the ones who understand how to combine tools, when to use which model, and how to design workflows that leverage multiple AI capabilities.
Start experimenting now:
- Try routing different types of tasks to different models
- Explore multi-agent frameworks like CrewAI or Microsoft AutoGen
- Learn about MCP and how to connect AI to external tools
- Practice breaking complex tasks into subtasks that could be parallelized
The single-chat-window era of AI is ending. What's coming is more powerful, more complex, and frankly more interesting.
What's Next?
This guide covers the foundations, but AI tools evolve fast. We'll keep updating this resource and publishing deep-dives on specific topics. Coming up:
- Detailed comparison of Claude vs. ChatGPT vs. Gemini for specific use cases
- Step-by-step RAG implementation guide for non-developers
- Prompt templates by profession (lawyers, marketers, developers, etc.)
- Cost optimization: getting maximum value without burning through API credits
If you've made it this far, you already know more about AI than 90% of people claiming to be experts. The difference now is applying it. Start with one concept, test it on real work, and build from there.
Questions? Suggestions for what we should cover next? Reach out--we're building this resource for professionals like you.