TOON Reduced Our Token Usage by 500 Tokens

Is TOON Going to Be the Next Feed Format for LLMs, or Even Replace JSON? (It’s for flat JSON data, not nested ones.) As LLMs evolve, efficiency in data representation becomes crucial. That’s where TOON (Token-Oriented Object Notation) steps in, a compact and LLM-friendly format designed to make data handling faster and cleaner. Why TOON stands out: -Uses less data than formatted JSON (especially in large arrays). -Built-in guardrails with explicit lengths and fields. -Cleaner syntax, no extra braces or quotes. -Indentation-based structure like YAML. -Handles tabular arrays efficiently, keys declared once, data flows as rows. Note: TOON works best with flat JSON data. If your data is nested, converting it directly into TOON can make it heavier and less efficient. Always flatten your JSON before using TOON. Could TOON become the next data format standard for AI models? #TOON #TOONFormat #TechInnovation #JSONAlternatives #LLMOptimization #CodingStandards #MinimalSyntax #AI #DevoutGrowth

JSON vs TOON : Why Data Engineers Are Buzzing About This New Format In the age of AI, every token counts – and that’s where this JSON vs TOON showdown gets interesting. JSON, the veteran data format we all know and love, can turn into a “token-hungry monster” in LLM prompts, with every quote, brace, and comma adding to your model’s bill. Enter TOON (Token-Oriented Object Notation), the lean newcomer that puts JSON on a diet by stripping away all those redundant symbols. The result? Often 30–60% fewer tokens for the same data, meaning you can feed models like GPT more info with less cost. Technically, TOON feels like YAML meets CSV – you declare a structure once and then list values, instead of repeating keys everywhere. For flat, uniform datasets (think user lists, logs, etc.), TOON shines with roughly 50% token savings in examples. This efficiency translates directly to cheaper and faster AI responses, which is why dev communities are hailing TOON as a “game-changer” for AI cost optimization. Now, JSON isn’t going away – it’s universal, battle-tested, and better for deeply nested or complex data (in fact, TOON can lose its edge with very nested structures). Plus, TOON is new and not an official standard, so you’ll need a handy library to use it in your code (unlike JSON, which every language speaks natively). Bottom line: JSON remains the go-to for everyday data exchange, but if you’re working with LLMs and watching those token counts, TOON might just become your favorite new sidekick for pumping more data into prompts with half the fuss (and cost). #DataEngineering #AI #MachineLearning #JSON #TOON #TechTrends

📊 The Future of Data Transfer: Introducing TOON (Token-Oriented Object Notation) 🚀 The AI era demands smarter data formats. Meet TOON – a compact, human-readable data serialization format specifically designed for Large Language Models. The Problem with JSON: JSON is fantastic for machine-to-machine communication, but it's verbose and token-expensive when feeding data to LLMs. Every token costs money. 💰 Meet TOON – The Game Changer: TOON achieves 30-60% fewer tokens than JSON by intelligently removing redundant syntax and repeating keys. Here's the real-world impact: Why This Matters: 💸 Cost Savings: For 10,000 API calls with large datasets, save $2,500+ monthly on GPT-4 and Claude APIs 🧠 Better AI Understanding: TOON achieves 70.1% accuracy on retrieval tasks (vs JSON's 65.4%) despite using fewer tokens ⚡ Efficient Context: Include more data in limited context windows without exceeding token limits What Makes TOON Different? ✨ Combines the best of CSV (tabular format) and YAML (indentation-based structure) ✨ Perfect for uniform data: arrays of objects with identical fields ✨ Removes unnecessary quotes, brackets, and redundant syntax ✨ Purpose-built for AI input, not output When to Use TOON: ✅ Sending database records to LLMs ✅ RAG (Retrieval-Augmented Generation) systems ✅ High-volume AI API calls ✅ Large datasets in AI prompts ✅ Cost-sensitive applications Keep JSON for: Standard APIs and storage Configuration files Complex, deeply nested data Non-uniform structures The Bottom Line: TOON isn't replacing JSON – it's complementing it. Use JSON programmatically, convert to TOON when talking to AI models. Simple but powerful. If you're building AI applications or working with LLM APIs, TOON could be your next efficiency breakthrough. 🎯 #AI #MachineLearning #DataEngineering #LLM #JSON #TOON #TechInnovation #CostOptimization #APIDesign #DataFormat

🚨 TOON: Useful, but don’t just follow the hype In LLM workflows, TOON promises 30–60% fewer tokens by replacing verbose JSON syntax. But here’s the catch: • It shines when your data is uniform arrays of objects. • It offers less benefit—or even downside—for deeply nested or mixed-structure data. • JSON’s ecosystem, interoperability and tooling remain hard to beat in general use. 💡 My takeaway for AI/Data engineers If you’re feeding large, flat datasets into an LLM (e.g., logs, rows, tables) → benchmark TOON vs JSON. If your data is complex, nested or part of wider systems → use JSON as default, consider TOON only at the last-mile prompt stage. In short: TOON is a smart optimisation tool, not a one-size-fits-all replacement for JSON. #ArtificialIntelligence #DataEngineering #PromptEngineering #LLM #AI #MachineLearning #FormatOptimization #TechTools #DataFormat #JSON #TOON

🔍 JSON vs TOON: Cut Token Costs by 60% JSON is the format we’ve all been using for LLMs, but TOON changes the game. It’s lighter, faster, and cuts token usage by up to 60%. Let’s break it down. 👇 🔁 JSON Repeats field names in every row 🧱 JSON Uses lots of syntactic characters ({}, [], " ") 💸 JSON Directly increases token cost and latency 😢 At scale, these small inefficiencies turn into big costs. 🚀 TOON is a Token-Optimized, AI-Native Data Format TOON (Token-Oriented Object Notation) was designed specifically for LLM communication. It combines: ✔ YAML-style indentation for structure ✔ CSV-style tabular headers to avoid repeating keys The result? 👉 40–60% fewer tokens than JSON 👉 Faster responses 👉 Lower API cost 👉 Cleaner, more compact data  👉LLMs parse TOON perfectly. ⚡ Example:   JSON: 25 lines for 5 records TOON: 6 lines for the same data 🚀 Try Toon to 💰 reduce your cost for API-based LLMs ⏲️ To have lower latency for chatbots, dashboards, and voice agents 📈 To scale better analyzing hundreds–thousands of rows per request 🤖 For multi-agent workflows where token savings multiply across steps 🧠 The Takeaway If JSON is killing your cost and performance, it might be time to rethink your data format. TOON is open-source (MIT), available across major languages Follow Maryam Bahrami for more insights on Generative AI and Agentic AI #AI #LLM #AIEngineering #Agents #DataEngineering #GenerativeAI #EnterpriseAI #JSON #TOON

🛑 Stop feeding your LLMs with giant JSON! It's costing you time and money. If you're building cool stuff with AI and still dumping huge, verbose JSON datasets into your prompts, check this out: TOON (The Token-Oriented Object Notation) Why TOON can be the New Standard for LLM Input: TOON is the lossless, token-efficient representation of the JSON data model, engineered specifically to cut down prompt size. It takes the best of YAML's indentation and CSV's tabular structure to eliminate syntactic noise. Key Features 💸 Token-efficient: typically 30–60% fewer tokens than JSON1 🤿 LLM-friendly guardrails: explicit lengths and fields enable validation 🍱 Minimal syntax: removes redundant punctuation (braces, brackets, most quotes) 📐 Indentation-based structure: like YAML, uses whitespace instead of braces 🧺 Tabular arrays: declare keys once, stream data as rows Check the Difference: JSON: { "users": [ { "id": 1, "name": "Alice", "role": "admin" }, { "id": 2, "name": "Bob", "role": "user" } ] } TOON: [3]{Id,Name,Salary}: 1,Alice,120000 2,Bob,95000 If your AI applications deal with high-volume, structured data, switching to TOON is the fastest way to lower your running costs and speed up inference. Time to stop paying for repeated punctuation! Github: https://lnkd.in/dDAGjUHF What do you think? Drop a comment below! 👇 #AI #LLM #Tokenization #PromptEngineering #TOON

⚡ JSON works… until your system needs to keep meaning intact The more work on AI-heavy workflows, the more one thing keeps showing up: JSON is perfect for storing data. But terrible at preserving the meaning of that data across multiple steps. And honestly, the problem isn’t obvious until you start chaining 5–6 transformations or running data through multiple AI components. Here’s what actually happens in real engineering life: Step 1: JSON goes in fine Step 2: transformation happens Step 3: context is gone Step 4: you patch something Step 5: retry because something broke Step 6: add another patch Step 7: now the whole thing is brittle None of this is JSON’s fault. It was built for APIs — not reasoning. And that’s exactly where TOON (Token-Oriented Object Notation) started making sense in my workflow. Not as a replacement. But as an add-on that carries the stuff JSON forgets. Here’s what I mean: JSON – stores the value – stores the structure – but loses the intent – loses the relationships – loses how this piece should behave in the next step TOON – stores the intent behind the value – stores what it interacts with – stores how it should be used – stores constraints – stores reasoning context These tiny bits of extra meaning literally reduce: - ambiguity - transformation errors - retries inside pipelines - silent logic breaks - unnecessary “if-else patches” - downstream confusion To be clear: I’m not dumping JSON. I’m just adding semantics where the system needs to understand instead of just receive. The way I see it: JSON is a container. TOON is context. Modern AI needs both. I made a quick diagram to show how the workflows behave in reality 👇 #FullStackDeveloper #AIEngineering #TOON #NeuroSymbolicAI #DeepTech #AIInfra #AgenticAI #BuildInPublic #SoftwareArchitecture #TechInnovation

TOON: Token-Oriented Object Notation: A Smarter Way to Feed Data into LLMs TOON is a data format designed to make structured data more token-efficient when working with Large Language Models (LLMs). Unlike JSON, which repeats field names and adds extra punctuation, TOON focuses on minimizing redundancy while still being easy to read. Example: JSON → [id: 1, name: User1, role: admin], [id: 2, name: User2, role: user] TOON → [2]{id,name,role}: 1,User1,admin 2,User2,user This structure can save around 30–60% of tokens for large, consistent datasets, which means lower API costs and more data per request. But here’s something interesting: TOON works best when your data is flat or tabular. If you feed it deeply nested JSON, the token count can actually increase slightly because of added formatting and indentation. ⚙️ Token usage (GPT-4 tokenizer) JSON (nested): 96 tokens TOON (nested): 103 tokens TOON (flattened/tabular): 63 tokens Key takeaway: If your data is structured and consistent, flatten it and use TOON - that’s where you’ll see real token savings. GitHub: https://lnkd.in/dXKNGxw5 Have you tried TOON in your LLM workflows? Would love to know how it performed for your use case. #LLM #AI #ArtificialIntelligence #MachineLearning #DeepLearning #TokenOptimization #PromptEngineering #DataEfficiency #AIEngineering #OpenAI #GenerativeAI #DataScience #AICommunity #JSON #TOON #DataSerialization #AIDevelopment #NaturalLanguageProcessing #AIResearch #TechInnovation #MLEngineering #LLMApplications #AITools

𝐓𝐎𝐎𝐍 (Token-Oriented Object Notation) What it is and why its getting attention? Lets Talk Instead of using verbose formatting in JSON with all those curly braces, colons, and quotes, TOON represents data in a way that's optimized for how LLMs actually process information. JSON example: {"name": "Anshul", "age": 24, "city": "DL"} TOON equivalent: name:Anshul age:24 city:DL Where 𝐓𝐎𝐎𝐍 accels at: 1. Token efficiency: When you're feeding data to an LLM, every token counts. TOON can reduce token usage by 30-40% compared to JSON. 2. Simpler prompts: Less visual clutter means the AI can focus on the actual data rather than parsing through formatting noise. 3. Speed: Fewer tokens = faster processing = happier users. BUT 𝐓𝐎𝐎𝐍 has some disadvantages: 1. Nested data = nightmare: Try representing complex nested structures in TOON and you'll see it starts to take more tokens than JSON per query. 2. No standard tooling: JSON has decades of battle-tested parsers, validators, and libraries in every language. TOON is very new 3. Human readability: While TOON looks clean in simple examples, complex data becomes very hard to read Simply Put -> TOON is a specialized tool for the AI-native workflow. If you're doing heavy LLM work and and you have flat JSON structure, it's worth experimenting with. or you can try flattening your JSON see how much tokens they both take You can watch this video from Piyush Garg for even better visual explanation https://lnkd.in/epBTTNV5 #AI #TOON #PROMPT #MachineLearning #LLM #TechInnovation #JSON #Optimization #AIEngineering

"Agentic AI" is the big buzzword right now. But what's really under the hood of these powerful new systems? This simple diagram is the best glossary I've seen for understanding this new architecture. A successful "Agent" isn't just a single LLM; it's a complete ecosystem of components that must work in perfect harmony: 🧠 1. The AI Agent (The Conductor): This is the central orchestrator. It takes the user's task and intelligently decides which tools (APIs, code) or knowledge (data) it needs to execute the request. 📚 2. The Vector Database (The Long-Term Memory / RAG): This is the core of RAG. To provide accurate, context-aware answers and avoid "hallucinations," the agent queries this specialized memory. This is exactly what I've been implementing using tools like Qdrant. 🗄️ 3. The Database (The Short-Term Memory): The agent also needs live, structured data, like "What's our current inventory?" or "What are this user's details?" This relies on experience with scaled SQL and MongoDB systems . ⚙️ 4. The LLM (The Reasoning Engine): This is the "thinking" part. A deep understanding of the model's internal architecture (like my experience building a GPT-2 from scratch ) is the key to truly optimizing this reasoning engine, not just using it as a black box. ▶️ 5. The Action (The "Hands"): Finally, the agent must do something in the real world (call an API, send an email, run code). This is where skills in model serving (like FastAPI ) and optimizing for low latency become critical. 🔄 6. The Feedback Loop (The "Learning"): The entire system must learn and improve. This is the very definition of MLOps—building automated pipelines (with tools like ZenML ) that constantly fine-tune and strengthen the system based on real-world feedback. This isn't just a theoretical diagram; it's the practical blueprint for building the next generation of AI products. What do you think is the single biggest challenge in implementing this full agentic stack? #AI #AgenticAI #RAG #LLM #GenerativeAI #MLOps #PyTorch #Qdrant #VectorDatabase #ArtificialIntelligence #Transformers