LLM integration with all the tabs

index.html

@@ -106,9 +106,20 @@
   </div>
 
   <h2 class="section-title">Interactive</h2>
-  <a href="/pages/chat.html" class="card-link">
+  <div class="ask-question-section">
+    <div class="ask-question-box">
+      <h3>💬 Ask a Question</h3>
+      <p style="color: var(--pink-700); font-size: 0.95rem; margin-bottom: 1rem;">Ask anything about AI — terminology, concepts, techniques, or real-world applications. Powered by your configured LLM.</p>
+      <div class="ask-question-input-row">
+        <input class="ask-question-input" id="askInput" placeholder="Ask me anything about AI..." />
+        <button class="llm-btn" onclick="askQuestion()" style="padding: 0.6rem 1.5rem;"><span class="icon">Send</span></button>
+      </div>
+      <div class="ask-question-answer" id="askAnswer"></div>
+    </div>
+  </div>
+  <a href="/pages/chat.html" class="card-link" style="margin-top: 1.5rem; display: block;">
     <div class="card">
-      <h3>💬 Chat</h3>
+      <h3>💬 Full Chat</h3>
       <p>Try AI right now — ask questions, brainstorm ideas, get explanations, or just experiment. Powered by a real LLM API.</p>
     </div>
   </a>
@@ -116,5 +127,57 @@
 
 <footer>AI Cheat Sheet &mdash; A learning reference for artificial intelligence</footer>
 
+<script src="lib/llm.js"></script>
+<script>
+(function(){
+  function askQuestion() {
+    var input = document.getElementById('askInput');
+    var answer = document.getElementById('askAnswer');
+    var text = input.value.trim();
+    if (!text) return;
+
+    answer.classList.add('visible');
+    answer.innerHTML = '<span class="llm-loading">Thinking...</span>';
+
+    var cheatSheetContext = `You are an AI educator answering questions based on this cheat sheet content:
+
+TERMINOLOGY: Machine Learning (ML), Supervised Learning, Unsupervised Learning, Reinforcement Learning, Overfitting, Underfitting, NLP, Token, Embedding, Context Window, Paraphrasing, Sentiment Analysis, LLM, Pre-trained Model, Fine-tuning, Parameters, Inference, Weights. Acronyms: AI, ML, DL, NLP, LLM, RLHF, RAG, API, SFT, PoC, GAN, CNN, AGI, STT/ASR, TTS.
+
+TECHNIQUES: Backpropagation, Epoch, Batch Size, Learning Rate, Transfer Learning, Data Augmentation, RLHF, SFT, Prompt Tuning, LoRA, Quantization, Distillation, Speculative Decoding, RAG, Agent/Tool Use, Chain-of-Thought.
+
+USE CASES: Content Generation, Image Generation, Video & Audio, Summarization, Code Generation, Debugging & Review, Documentation, Code Translation, Chatbots & Assistants, Data Analysis, Research & Search, Translation, Email & Meeting Assistants, Document Processing, Healthcare, Finance, Automotive, Education, Manufacturing, Legal.
+
+MODEL TYPES: LLMs (GPT-4, Claude, Gemini, Llama, Mistral), Encoder-Only (BERT, RoBERTa), Decoder-Only (GPT, Claude, Llama), Encoder-Decoder (T5, BART), CNN (ResNet, EfficientNet), ViT (CLIP, DINOv2), Diffusion (Stable Diffusion, DALL-E), GAN (StyleGAN), VQ-VAE, Flow Models, RNN/LSTM, MoE, Retrieval Models, SLMs (Phi-3, Gemma).
+
+PROMPT ENGINEERING: Zero-Shot, Few-Shot, Chain-of-Thought, Role Prompting, Structured Output, Self-Consistency, ReAct. Tips: Be specific, use delimiters, provide context, iterate.
+
+MATH & CONCEPTS: Attention, Self-Attention, Multi-Head Attention, Positional Encoding, FFN, Layer Norm, Loss Function, Gradient Descent, Adam, Gradient, Regularization, Batch Norm, Temperature, Top-K, Top-P, Greedy Decoding, Beam Search, Logits, Perplexity, Accuracy, Precision & Recall, F1 Score, BLEU/ROUGE, TPS. Formulas: Attention=softmax(QKᵀ/√dₖ)V, Cross-Entropy=-Σyᵢlog(pᵢ), Softmax=eˣⁱ/Σeˣʲ, ReLU=max(0,x), LayerNorm=(x-μ)/σ×γ+β, F1=2×(P×R)/(P+R), Perplexity=2^(cross-entropy).`;
+
+    var messages = [
+      { role: 'system', content: 'You are a helpful AI educator answering questions based on an AI cheat sheet. Use the context below to provide accurate, concise answers. If a question is outside the cheat sheet scope, say so but try to help anyway. Keep answers to 2-4 short paragraphs. Use formatting like bold text and code blocks where helpful.' },
+      { role: 'user', content: cheatSheetContext + '\n\nQuestion: ' + text }
+    ];
+
+    LLM.chatWithHistory('askAnswer', messages)
+      .then(function() {
+        answer.innerHTML = answer.innerHTML.replace('<span class="llm-loading">Thinking...</span>', '');
+      })
+      .catch(function() {});
+  }
+
+  var askInput = document.getElementById('askInput');
+  if (askInput) {
+    askInput.addEventListener('keydown', function(e) {
+      if (e.key === 'Enter') {
+        e.preventDefault();
+        askQuestion();
+      }
+    });
+  }
+
+  window.askQuestion = askQuestion;
+})();
+</script>
+
 </body>
 </html>