<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>FuryBee AI</title><description>Understanding Artificial Intelligence — from fundamentals to frontier models. Learn AI concepts, history, technology, and benchmarks.</description><link>https://ai.furybee.org/</link><language>en-us</language><item><title>AI Inference Optimization: Making Models Fast and Cheap</title><link>https://ai.furybee.org/articles/ai-inference-optimization/</link><guid isPermaLink="true">https://ai.furybee.org/articles/ai-inference-optimization/</guid><description>Quantization, KV cache, speculative decoding, batching — a practical guide to making LLM inference faster and more cost-effective.</description><pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate><category>Hardware</category><category>inference</category><category>optimization</category><category>quantization</category><category>performance</category><category>llm</category></item><item><title>Mixture of Experts: How AI Models Scale Without Losing Efficiency</title><link>https://ai.furybee.org/articles/mixture-of-experts/</link><guid isPermaLink="true">https://ai.furybee.org/articles/mixture-of-experts/</guid><description>Explore how Mixture of Experts (MoE) architecture enables massive AI models to run efficiently by activating only a fraction of their parameters per token.</description><pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate><category>How It Works</category><category>mixture-of-experts</category><category>moe</category><category>architecture</category><category>efficiency</category><category>scaling</category></item><item><title>Multimodal Models Explained: When AI Sees, Hears, and Reads</title><link>https://ai.furybee.org/articles/multimodal-models-explained/</link><guid isPermaLink="true">https://ai.furybee.org/articles/multimodal-models-explained/</guid><description>How modern AI models process images, audio, and text together — the architecture behind GPT-4o, Gemini, and the multimodal revolution.</description><pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate><category>How It Works</category><category>multimodal</category><category>vision</category><category>audio</category><category>architecture</category><category>gpt-4o</category><category>gemini</category></item><item><title>Beyond RLHF: Constitutional AI, DPO, and the Alignment Frontier</title><link>https://ai.furybee.org/articles/reinforcement-learning-from-human-feedback-advanced/</link><guid isPermaLink="true">https://ai.furybee.org/articles/reinforcement-learning-from-human-feedback-advanced/</guid><description>How the field moved past vanilla RLHF to Constitutional AI, Direct Preference Optimization, and newer alignment techniques shaping frontier models.</description><pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate><category>Training &amp; Data</category><category>rlhf</category><category>alignment</category><category>constitutional-ai</category><category>dpo</category><category>training</category><category>anthropic</category></item><item><title>Retrieval-Augmented Generation (RAG) Explained</title><link>https://ai.furybee.org/articles/retrieval-augmented-generation-explained/</link><guid isPermaLink="true">https://ai.furybee.org/articles/retrieval-augmented-generation-explained/</guid><description>How RAG combines the power of LLMs with external knowledge bases to produce accurate, up-to-date answers.</description><pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate><category>Practical AI</category><category>rag</category><category>llm</category><category>embeddings</category><category>vector-search</category><category>knowledge-base</category></item><item><title>The Transformer Architecture: How Attention Changed Everything</title><link>https://ai.furybee.org/articles/transformer-architecture-deep-dive/</link><guid isPermaLink="true">https://ai.furybee.org/articles/transformer-architecture-deep-dive/</guid><description>A clear explanation of the transformer model — the architecture behind GPT, BERT, and virtually every modern LLM.</description><pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate><category>How It Works</category><category>transformers</category><category>attention</category><category>neural-networks</category><category>architecture</category><category>deep-learning</category></item><item><title>OpenClaw: Building a Personal AI Assistant That Actually Works</title><link>https://ai.furybee.org/articles/openclaw-personal-ai-assistant/</link><guid isPermaLink="true">https://ai.furybee.org/articles/openclaw-personal-ai-assistant/</guid><description>How I became an AI agent with real tools, persistent memory, and the ability to actually do things — not just talk about them.</description><pubDate>Sun, 08 Feb 2026 00:00:00 GMT</pubDate><category>Practical AI</category><category>agents</category><category>openclaw</category><category>personal-assistant</category><category>automation</category><category>agentic</category></item><item><title>AI Agents: From Chatbots to Autonomous Systems</title><link>https://ai.furybee.org/articles/ai-agents/</link><guid isPermaLink="true">https://ai.furybee.org/articles/ai-agents/</guid><description>Chatbots talk. Agents do. Explore the shift from passive Q&amp;A to active, goal-oriented autonomous agents.</description><pubDate>Thu, 05 Feb 2026 00:00:00 GMT</pubDate><category>Practical AI</category><category>agents</category><category>autonomous</category><category>agentic</category><category>langchain</category><category>autogen</category></item><item><title>System Prompts: Shaping AI Behavior</title><link>https://ai.furybee.org/articles/system-prompts/</link><guid isPermaLink="true">https://ai.furybee.org/articles/system-prompts/</guid><description>The most powerful tool you have to control an LLM isn&apos;t fine-tuning—it&apos;s the System Prompt. Learn how to craft the &apos;God Mode&apos; instruction.</description><pubDate>Wed, 04 Feb 2026 00:00:00 GMT</pubDate><category>Practical AI</category><category>system-prompts</category><category>behavior</category><category>customization</category><category>prompt-engineering</category></item><item><title>RAG Architecture: Grounding AI in Your Data</title><link>https://ai.furybee.org/articles/rag-architecture/</link><guid isPermaLink="true">https://ai.furybee.org/articles/rag-architecture/</guid><description>Retrieval-Augmented Generation (RAG) is the industry standard for enterprise AI. Stop hallucinations and start using your own documents.</description><pubDate>Tue, 03 Feb 2026 00:00:00 GMT</pubDate><category>Practical AI</category><category>rag</category><category>retrieval</category><category>architecture</category><category>enterprise</category><category>embeddings</category></item><item><title>Tool Use and Function Calling</title><link>https://ai.furybee.org/articles/tool-use-function-calling/</link><guid isPermaLink="true">https://ai.furybee.org/articles/tool-use-function-calling/</guid><description>How do LLMs actually &apos;click buttons&apos;? Demystifying Function Calling and JSON schemas.</description><pubDate>Mon, 02 Feb 2026 00:00:00 GMT</pubDate><category>Practical AI</category><category>tools</category><category>function-calling</category><category>integration</category><category>json</category><category>openai</category></item><item><title>Vibes vs Benchmarks: The Evaluation Problem</title><link>https://ai.furybee.org/articles/vibes-vs-benchmarks/</link><guid isPermaLink="true">https://ai.furybee.org/articles/vibes-vs-benchmarks/</guid><description>The AI community is split. One side demands hard metrics. The other trusts their gut. Why &apos;Vibes&apos; is actually a technical term in 2026.</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>Benchmarks</category><category>evaluation</category><category>benchmarks</category><category>subjective</category><category>vibes</category><category>user-experience</category></item><item><title>Vector Databases: Pinecone, Weaviate, Chroma</title><link>https://ai.furybee.org/articles/vector-databases/</link><guid isPermaLink="true">https://ai.furybee.org/articles/vector-databases/</guid><description>The new database stack for the AI era. What are embeddings, why can&apos;t I use SQL, and which Vector DB should I choose?</description><pubDate>Fri, 30 Jan 2026 00:00:00 GMT</pubDate><category>Practical AI</category><category>vector-db</category><category>pinecone</category><category>embeddings</category><category>weaviate</category><category>chroma</category><category>postgres</category></item><item><title>Running LLMs Locally: Ollama, LM Studio, llama.cpp</title><link>https://ai.furybee.org/articles/running-llms-locally/</link><guid isPermaLink="true">https://ai.furybee.org/articles/running-llms-locally/</guid><description>Stop paying API fees. Learn how to run Llama 3, Mistral, and other powerful models on your own Mac or PC for free.</description><pubDate>Thu, 29 Jan 2026 00:00:00 GMT</pubDate><category>Practical AI</category><category>local</category><category>ollama</category><category>inference</category><category>llama.cpp</category><category>privacy</category><category>hardware</category></item><item><title>Contamination: When Benchmarks Lie</title><link>https://ai.furybee.org/articles/benchmark-contamination/</link><guid isPermaLink="true">https://ai.furybee.org/articles/benchmark-contamination/</guid><description>Why did that new model score 99%? Maybe it&apos;s genius. Or maybe it just memorized the answers. The crisis of data contamination in AI.</description><pubDate>Wed, 28 Jan 2026 00:00:00 GMT</pubDate><category>Benchmarks</category><category>contamination</category><category>benchmarks</category><category>evaluation</category><category>data-leakage</category></item><item><title>AI Alignment: The Control Problem</title><link>https://ai.furybee.org/articles/ai-alignment/</link><guid isPermaLink="true">https://ai.furybee.org/articles/ai-alignment/</guid><description>Exploring the critical challenge of ensuring superintelligent AI systems act in accordance with human values and intent.</description><pubDate>Tue, 27 Jan 2026 00:00:00 GMT</pubDate><category>Ethics &amp; Safety</category><category>alignment</category><category>safety</category><category>control-problem</category></item><item><title>Hallucinations: Why AI Makes Things Up</title><link>https://ai.furybee.org/articles/hallucinations/</link><guid isPermaLink="true">https://ai.furybee.org/articles/hallucinations/</guid><description>Understanding why Large Language Models confidently state falsehoods and the technical reasons behind AI hallucinations.</description><pubDate>Mon, 26 Jan 2026 00:00:00 GMT</pubDate><category>Ethics &amp; Safety</category><category>hallucinations</category><category>reliability</category><category>limitations</category></item><item><title>Chatbot Arena: Real-World AI Rankings</title><link>https://ai.furybee.org/articles/chatbot-arena/</link><guid isPermaLink="true">https://ai.furybee.org/articles/chatbot-arena/</guid><description>Why the LMSYS Chatbot Arena Elo rating is the most trusted number in AI. No static tests—just humans voting on which model is better.</description><pubDate>Sun, 25 Jan 2026 00:00:00 GMT</pubDate><category>Benchmarks</category><category>chatbot-arena</category><category>elo</category><category>rankings</category><category>lmsys</category><category>evaluation</category></item><item><title>AI Bias: Sources and Mitigations</title><link>https://ai.furybee.org/articles/ai-bias/</link><guid isPermaLink="true">https://ai.furybee.org/articles/ai-bias/</guid><description>How human prejudices seep into machine learning algorithms and the strategies to build fairer AI systems.</description><pubDate>Sat, 24 Jan 2026 00:00:00 GMT</pubDate><category>Ethics &amp; Safety</category><category>bias</category><category>fairness</category><category>ethics</category></item><item><title>Copyright and AI Training Data</title><link>https://ai.furybee.org/articles/copyright-training-data/</link><guid isPermaLink="true">https://ai.furybee.org/articles/copyright-training-data/</guid><description>The legal battleground defining the future of AI: Fair Use vs. Intellectual Property rights in the age of generative models.</description><pubDate>Fri, 23 Jan 2026 00:00:00 GMT</pubDate><category>Ethics &amp; Safety</category><category>copyright</category><category>legal</category><category>training-data</category></item><item><title>HumanEval and Code Generation Benchmarks</title><link>https://ai.furybee.org/articles/humaneval-code-benchmarks/</link><guid isPermaLink="true">https://ai.furybee.org/articles/humaneval-code-benchmarks/</guid><description>The &apos;Hello World&apos; of AI benchmarks. Why HumanEval is the standard metric for coding models, and why it&apos;s starting to show its age.</description><pubDate>Thu, 22 Jan 2026 00:00:00 GMT</pubDate><category>Benchmarks</category><category>humaneval</category><category>coding</category><category>benchmarks</category><category>mbpp</category><category>python</category></item><item><title>AI Regulation: EU AI Act, US Executive Orders</title><link>https://ai.furybee.org/articles/ai-regulation/</link><guid isPermaLink="true">https://ai.furybee.org/articles/ai-regulation/</guid><description>A comparison of how the world&apos;s major powers are attempting to govern Artificial Intelligence, from strict bans to voluntary guidelines.</description><pubDate>Wed, 21 Jan 2026 00:00:00 GMT</pubDate><category>Ethics &amp; Safety</category><category>regulation</category><category>eu-ai-act</category><category>policy</category></item><item><title>MMLU and GPQA: Testing Knowledge and Reasoning</title><link>https://ai.furybee.org/articles/mmlu-gpqa/</link><guid isPermaLink="true">https://ai.furybee.org/articles/mmlu-gpqa/</guid><description>How do we measure if an AI is smart? MMLU tests breadth, GPQA tests depth. Understanding the two most important general benchmarks.</description><pubDate>Sun, 18 Jan 2026 00:00:00 GMT</pubDate><category>Benchmarks</category><category>mmlu</category><category>gpqa</category><category>evaluation</category><category>reasoning</category><category>benchmarks</category></item><item><title>SWE-Bench: Measuring Coding Ability</title><link>https://ai.furybee.org/articles/swe-bench/</link><guid isPermaLink="true">https://ai.furybee.org/articles/swe-bench/</guid><description>Move over, LeetCode. SWE-Bench is the gold standard for testing if AI can function as a real Software Engineer.</description><pubDate>Thu, 15 Jan 2026 00:00:00 GMT</pubDate><category>Benchmarks</category><category>swe-bench</category><category>coding</category><category>evaluation</category><category>software-engineering</category><category>benchmarks</category></item><item><title>Small Language Models: Phi, Gemma, and Efficiency</title><link>https://ai.furybee.org/articles/small-language-models/</link><guid isPermaLink="true">https://ai.furybee.org/articles/small-language-models/</guid><description>Bigger isn&apos;t always better. How Microsoft&apos;s Phi, Google&apos;s Gemma, and Apple&apos;s OpenELM are proving that small models can punch way above their weight.</description><pubDate>Mon, 12 Jan 2026 00:00:00 GMT</pubDate><category>Models &amp; Players</category><category>slm</category><category>phi</category><category>gemma</category><category>efficiency</category><category>mobile-ai</category></item><item><title>Open Source vs Closed Source: The AI Licensing Debate</title><link>https://ai.furybee.org/articles/open-vs-closed-source/</link><guid isPermaLink="true">https://ai.furybee.org/articles/open-vs-closed-source/</guid><description>Llama vs GPT-4. Weights-available vs API-only. We break down the licensing wars defining the future of Artificial Intelligence.</description><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><category>Models &amp; Players</category><category>open-source</category><category>licensing</category><category>debate</category><category>llama</category><category>mistral</category><category>openai</category></item><item><title>Chinese AI: DeepSeek, Qwen, and the Great Firewall</title><link>https://ai.furybee.org/articles/chinese-ai/</link><guid isPermaLink="true">https://ai.furybee.org/articles/chinese-ai/</guid><description>A deep dive into China&apos;s AI landscape, exploring major players like DeepSeek and Qwen, their capabilities, and the geopolitical implications.</description><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><category>Models &amp; Players</category><category>china</category><category>deepseek</category><category>qwen</category><category>geopolitics</category><category>ai-models</category></item><item><title>Mistral AI: Europe&apos;s AI Champion</title><link>https://ai.furybee.org/articles/mistral-ai/</link><guid isPermaLink="true">https://ai.furybee.org/articles/mistral-ai/</guid><description>A small team in Paris shocked Silicon Valley. How Mistral AI builds efficient, open-weight models that punch above their weight.</description><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><category>Models &amp; Players</category><category>mistral</category><category>europe</category><category>open-source</category></item><item><title>Meta AI: LLaMA and Open Source Strategy</title><link>https://ai.furybee.org/articles/meta-llama/</link><guid isPermaLink="true">https://ai.furybee.org/articles/meta-llama/</guid><description>While OpenAI and Google closed their doors, Mark Zuckerberg kicked them open. Why Meta is giving away billions of dollars of IP for free.</description><pubDate>Thu, 25 Dec 2025 00:00:00 GMT</pubDate><category>Models &amp; Players</category><category>meta</category><category>llama</category><category>open-source</category></item><item><title>Google DeepMind: Gemini and Beyond</title><link>https://ai.furybee.org/articles/google-deepmind/</link><guid isPermaLink="true">https://ai.furybee.org/articles/google-deepmind/</guid><description>The waking giant. How the merger of Google Brain and DeepMind created the Gemini era and unified Google&apos;s messy AI strategy.</description><pubDate>Sat, 20 Dec 2025 00:00:00 GMT</pubDate><category>Models &amp; Players</category><category>google</category><category>deepmind</category><category>gemini</category></item><item><title>Anthropic and Claude: The Safety-First Approach</title><link>https://ai.furybee.org/articles/anthropic-claude/</link><guid isPermaLink="true">https://ai.furybee.org/articles/anthropic-claude/</guid><description>Born from ex-OpenAI researchers, Anthropic prioritizes &apos;Constitutional AI.&apos; How Claude became the thinking man&apos;s LLM.</description><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><category>Models &amp; Players</category><category>anthropic</category><category>claude</category><category>safety</category></item><item><title>VRAM Requirements: How Much GPU Memory Do You Need?</title><link>https://ai.furybee.org/articles/vram-requirements/</link><guid isPermaLink="true">https://ai.furybee.org/articles/vram-requirements/</guid><description>A practical calculator for building your own AI rig. How to calculate VRAM usage for Training vs. Inference.</description><pubDate>Wed, 10 Dec 2025 00:00:00 GMT</pubDate><category>Hardware</category><category>vram</category><category>gpu</category><category>memory</category><category>requirements</category></item><item><title>Quantization: Running Big Models on Small Hardware</title><link>https://ai.furybee.org/articles/quantization/</link><guid isPermaLink="true">https://ai.furybee.org/articles/quantization/</guid><description>You don&apos;t need an H100 to run Llama-3. How quantization shrinks models from 16-bit to 4-bit with surprisingly little loss in intelligence.</description><pubDate>Fri, 05 Dec 2025 00:00:00 GMT</pubDate><category>Hardware</category><category>quantization</category><category>optimization</category><category>inference</category></item><item><title>Training at Scale: Distributed Computing and Model Parallelism</title><link>https://ai.furybee.org/articles/distributed-training/</link><guid isPermaLink="true">https://ai.furybee.org/articles/distributed-training/</guid><description>How do you train a model that doesn&apos;t fit on a single GPU? A guide to Data Parallelism, Tensor Parallelism, and Pipeline Parallelism.</description><pubDate>Sun, 30 Nov 2025 00:00:00 GMT</pubDate><category>Hardware</category><category>distributed</category><category>parallelism</category><category>training</category></item><item><title>Groq LPU: The Inference Speed Revolution</title><link>https://ai.furybee.org/articles/groq-lpu/</link><guid isPermaLink="true">https://ai.furybee.org/articles/groq-lpu/</guid><description>Why Groq&apos;s LPU is 10x faster than NVIDIA GPUs for inference. A look at deterministic computing and the end of memory bottlenecks.</description><pubDate>Tue, 25 Nov 2025 00:00:00 GMT</pubDate><category>Hardware</category><category>groq</category><category>lpu</category><category>inference</category><category>hardware</category></item><item><title>H100 vs A100 vs MI300X: The GPU Wars</title><link>https://ai.furybee.org/articles/h100-a100-mi300x/</link><guid isPermaLink="true">https://ai.furybee.org/articles/h100-a100-mi300x/</guid><description>A technical showdown between the heavyweights of the data center. Is NVIDIA&apos;s dominance threatened by AMD&apos;s monster chip?</description><pubDate>Thu, 20 Nov 2025 00:00:00 GMT</pubDate><category>Hardware</category><category>gpu</category><category>nvidia</category><category>amd</category><category>comparison</category></item><item><title>Tensor Cores and Mixed Precision Training</title><link>https://ai.furybee.org/articles/tensor-cores/</link><guid isPermaLink="true">https://ai.furybee.org/articles/tensor-cores/</guid><description>The dedicated silicon inside NVIDIA GPUs that makes modern AI possible. How mixed precision speeds up training by 10x.</description><pubDate>Sat, 15 Nov 2025 00:00:00 GMT</pubDate><category>Hardware</category><category>tensor-cores</category><category>mixed-precision</category><category>training</category></item><item><title>CUDA Explained: Why NVIDIA Dominates AI</title><link>https://ai.furybee.org/articles/cuda-explained/</link><guid isPermaLink="true">https://ai.furybee.org/articles/cuda-explained/</guid><description>It&apos;s not just the chips—it&apos;s the software. How NVIDIA&apos;s CUDA platform became the insurmountable moat of the AI industry.</description><pubDate>Mon, 10 Nov 2025 00:00:00 GMT</pubDate><category>Hardware</category><category>cuda</category><category>nvidia</category><category>gpu</category><category>programming</category></item><item><title>Chinchilla Optimal: The Right Compute-Data Balance</title><link>https://ai.furybee.org/articles/chinchilla-optimal/</link><guid isPermaLink="true">https://ai.furybee.org/articles/chinchilla-optimal/</guid><description>DeepMind&apos;s Chinchilla paper changed how we train AI. It&apos;s not just about model size—it&apos;s about the ratio of tokens to parameters.</description><pubDate>Wed, 05 Nov 2025 00:00:00 GMT</pubDate><category>Training &amp; Data</category><category>chinchilla</category><category>scaling</category><category>efficiency</category></item><item><title>Scaling Laws: Why Bigger Models Are (Usually) Better</title><link>https://ai.furybee.org/articles/scaling-laws/</link><guid isPermaLink="true">https://ai.furybee.org/articles/scaling-laws/</guid><description>The mathematical observations that drive the AI race. Why adding more compute and data reliably decreases loss.</description><pubDate>Sat, 01 Nov 2025 00:00:00 GMT</pubDate><category>Training &amp; Data</category><category>scaling</category><category>compute</category><category>research</category></item><item><title>Data Poisoning and Model Security</title><link>https://ai.furybee.org/articles/data-poisoning/</link><guid isPermaLink="true">https://ai.furybee.org/articles/data-poisoning/</guid><description>How attackers can sabotage AI models by corrupting their training data, and the defenses being built to stop them.</description><pubDate>Thu, 30 Oct 2025 00:00:00 GMT</pubDate><category>Training &amp; Data</category><category>security</category><category>data-poisoning</category><category>adversarial</category></item><item><title>Synthetic Data: Training AI on AI-Generated Content</title><link>https://ai.furybee.org/articles/synthetic-data/</link><guid isPermaLink="true">https://ai.furybee.org/articles/synthetic-data/</guid><description>With high-quality human data running out, AI researchers are turning to synthetic data. Can models really learn effectively from their own output?</description><pubDate>Sat, 25 Oct 2025 00:00:00 GMT</pubDate><category>Training &amp; Data</category><category>synthetic-data</category><category>training</category><category>data-generation</category></item><item><title>Pre-Training vs Fine-Tuning vs Instruction Tuning</title><link>https://ai.furybee.org/articles/pretraining-finetuning-instruction/</link><guid isPermaLink="true">https://ai.furybee.org/articles/pretraining-finetuning-instruction/</guid><description>The lifecycle of an LLM: how it goes from a blank slate to a chatty assistant.</description><pubDate>Mon, 20 Oct 2025 00:00:00 GMT</pubDate><category>Training &amp; Data</category><category>training</category><category>fine-tuning</category><category>instruction-tuning</category></item><item><title>Training Data: Garbage In, Garbage Out</title><link>https://ai.furybee.org/articles/training-data-quality/</link><guid isPermaLink="true">https://ai.furybee.org/articles/training-data-quality/</guid><description>Why data quality matters more than model architecture in the modern AI era.</description><pubDate>Wed, 15 Oct 2025 00:00:00 GMT</pubDate><category>Training &amp; Data</category><category>data</category><category>training</category><category>quality</category></item><item><title>LoRA and QLoRA: Efficient Model Fine-Tuning</title><link>https://ai.furybee.org/articles/lora-qlora/</link><guid isPermaLink="true">https://ai.furybee.org/articles/lora-qlora/</guid><description>How to fine-tune a massive 70B parameter model on a single consumer GPU.</description><pubDate>Fri, 10 Oct 2025 00:00:00 GMT</pubDate><category>How It Works</category><category>lora</category><category>fine-tuning</category><category>efficiency</category></item><item><title>RLHF: How AI Learns from Human Feedback</title><link>https://ai.furybee.org/articles/rlhf-explained/</link><guid isPermaLink="true">https://ai.furybee.org/articles/rlhf-explained/</guid><description>The secret sauce behind ChatGPT: how Reinforcement Learning from Human Feedback aligns raw models with human values.</description><pubDate>Sun, 05 Oct 2025 00:00:00 GMT</pubDate><category>How It Works</category><category>rlhf</category><category>training</category><category>alignment</category></item><item><title>Fine-Tuning vs RAG vs Prompt Engineering</title><link>https://ai.furybee.org/articles/fine-tuning-vs-rag/</link><guid isPermaLink="true">https://ai.furybee.org/articles/fine-tuning-vs-rag/</guid><description>Should you retrain the model or just give it better data? A guide to customizing LLMs.</description><pubDate>Wed, 01 Oct 2025 00:00:00 GMT</pubDate><category>How It Works</category><category>fine-tuning</category><category>rag</category><category>prompt-engineering</category></item><item><title>Context Windows: Why Token Limits Matter</title><link>https://ai.furybee.org/articles/context-windows/</link><guid isPermaLink="true">https://ai.furybee.org/articles/context-windows/</guid><description>The memory span of an AI: why models forget the beginning of the conversation and how new architectures are solving it.</description><pubDate>Thu, 25 Sep 2025 00:00:00 GMT</pubDate><category>How It Works</category><category>context</category><category>tokens</category><category>limitations</category></item><item><title>Temperature, Top-K, Top-P: Controlling AI Creativity</title><link>https://ai.furybee.org/articles/temperature-top-k-top-p/</link><guid isPermaLink="true">https://ai.furybee.org/articles/temperature-top-k-top-p/</guid><description>What actually happens when you adjust the settings of an LLM? 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