The Complete AI & LLM Roadmap 2026: 80+ Topics to Master Modern AI Engineering

Why Most Engineers Fail AI/LLM Interviews

You've fine-tuned GPT models at work. You've built RAG systems. You've even deployed LLM-powered chatbots to production.

But when the interviewer asks you to "explain the attention mechanism from first principles" or "design a production RAG system that handles 1M queries per day," you freeze.

Sound familiar?

Here's the brutal truth: AI interviews aren't about knowing how to call OpenAI APIs. They're about understanding fundamental building blocks and knowing when to apply them.

Companies like Google, Meta, OpenAI, and Anthropic don't want API wrappers. They want engineers who deeply understand transformers, can optimize inference latency, design multi-agent systems, and reason about safety trade-offs.

What if you had a complete roadmap of every AI/LLM concept that actually matters — organized, structured, and ready to master?

That's exactly what this guide delivers.

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What You'll Learn

This comprehensive roadmap breaks down 80+ essential AI and LLM topics organized into 13 fundamental categories that form the backbone of modern AI engineering at companies like OpenAI, Anthropic, Google DeepMind, and Meta AI.

By the end, you'll not only understand these concepts but also know how to practice them interactively with AI coaching on Thita.ai — where you can solve real AI problems, get explanations, and simulate actual AI engineering interviews.

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What Is an AI/LLM Roadmap?

Think of this roadmap as your complete knowledge graph for AI engineering mastery.

Instead of randomly jumping between YouTube tutorials on attention mechanisms, RAG systems, and prompt engineering, you follow a structured path that builds knowledge progressively — from neural network fundamentals to production GenAI system design.

Example Learning Flow:

• Start with Neural Network Foundations → Understand Transformer Architecture → Master LLM Internals → Build RAG Systems → Design AI Agents → Scale Production Systems

This systematic approach is exactly how senior ML engineers at top AI companies prepare. They don't cram random tutorials — they build comprehensive mental models through structured learning.

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The 13 Core Categories: Your Learning Path

Each category represents a critical pillar of modern AI/LLM engineering. Below, we break down all 13 categories with their essential topics.

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1. Neural Network Fundamentals (6 Topics)

Foundation: Understanding the building blocks of all modern AI

Essential Topics:

• Forward and Backward Propagation: Chain rule application, gradient computation through layers
• Optimization Algorithms: Gradient descent, SGD, Adam, RMSprop, AdaGrad, learning rate scheduling
• Activation Functions: ReLU family (LeakyReLU, PReLU, GELU), Swish, sigmoid, tanh — when to use each
• Loss Functions: Cross-entropy for classification, MSE for regression, contrastive loss for embeddings
• Regularization Techniques: Dropout, L1/L2 weight decay, early stopping, data augmentation
• Normalization Methods: Batch normalization, layer normalization, group normalization, RMSNorm

Why This Matters: You can't truly understand transformers and LLMs without solid neural network foundations. Every interview at AI companies tests this.

Real Interview Question: "Explain backpropagation step-by-step. Why does batch normalization help training?"

💡 Pro Tip: Be able to derive gradients by hand for simple networks. Interviewers test depth of understanding.

⚠️ Common Mistake: Memorizing formulas without understanding the why behind each technique.

Practice on Thita.ai: Start with Neural Network Foundations →

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2. Transformer Architecture Deep Dive (8 Topics)

Foundation: The backbone of every modern AI system

Essential Topics:

• Self-Attention Mechanism: Query, Key, Value matrices; scaled dot-product attention; attention score computation
• Multi-Head Attention: Parallel attention heads, why multiple heads matter, concatenation and projection
• Positional Encoding: Sinusoidal encoding, learned positional embeddings, rotary position embeddings (RoPE)
• Feed-Forward Networks: Position-wise FFN, expansion ratio (typically 4x), activation functions
• Layer Normalization: Pre-norm vs post-norm placement, stability implications
• Architecture Variants: Encoder-only (BERT), decoder-only (GPT), encoder-decoder (T5) — when to use each
• Attention Optimizations: Sparse attention, linear attention, Flash Attention, multi-query attention (MQA)
• Efficiency Improvements: Flash Attention 2, PagedAttention, grouped-query attention (GQA)

Why This Is Critical: Transformers power GPT-4, Claude, Gemini, Llama — literally every modern LLM. This is the most frequently tested topic in 2026 AI interviews.

💡 Pro Tip: Draw the attention mechanism on a whiteboard. Visual explanations demonstrate deep understanding.

⚠️ Common Mistake: Not understanding why scaled dot-product (dividing by √d_k) prevents gradient saturation.

Real Interview Question: "Walk me through how self-attention works. Why is it better than RNNs for language modeling?"

Practice on Thita.ai: Master Transformer Architecture →

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3. Large Language Models (LLMs) (7 Topics)

Foundation: Understanding GPT, Claude, Llama, and modern LLMs

Essential Topics:

• Pretraining Objectives: Causal language modeling (next token prediction), masked language modeling (BERT-style), prefix LM
• Tokenization Strategies: Byte-Pair Encoding (BPE), WordPiece, SentencePiece, Unigram — trade-offs and vocabulary size
• Decoding Strategies: Greedy decoding, beam search, nucleus sampling (top-p), top-k sampling, temperature scaling
• Context Window Management: Truncation strategies, sliding window attention, hierarchical attention
• Long Context Models: Longformer sparse attention, BigBird block-sparse attention, ALiBi position encoding, YaRN
• KV Cache Optimization: Key-value caching for faster inference, memory-bandwidth trade-offs
• Scaling Laws: Chinchilla optimal scaling, compute-optimal training, parameter count vs training tokens

Why This Matters: LLMs are the foundation of 95% of AI applications in 2026. Understanding their internals is non-negotiable.

💡 Pro Tip: Know when to use beam search (translation) vs sampling (creative writing) vs greedy (factual QA).

Real Interview Question: "You need to deploy GPT for production with <100ms latency. What optimizations do you apply?"

Common Pattern: Questions about inference optimization, cost reduction, and context window limitations are standard.

Practice on Thita.ai: Deep Dive into LLMs →

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4. Retrieval-Augmented Generation (RAG) (7 Topics)

Foundation: The most common production LLM pattern in 2026

Essential Topics:

• Dense Retrieval Fundamentals: Semantic search with embeddings, bi-encoder architecture, retrieval vs generation
• Vector Databases: FAISS, Pinecone, Weaviate, Chroma; indexing algorithms (HNSW, IVF); sharding and replication
• Document Chunking Strategies: Fixed-size chunking, semantic chunking, recursive text splitting, agentic chunking
• Embedding Models: Sentence transformers, OpenAI text-embedding-ada-002, Cohere Embed, BGE, E5
• Context Injection: Prompt construction with retrieved context, context ordering, relevance filtering
• Reranking Techniques: Cross-encoder reranking, maximum marginal relevance (MMR), diversity-aware retrieval
• RAG Evaluation: Faithfulness, answer relevance, context precision, context recall, RAGAS framework

Why This Is Critical: 70% of production LLM applications use RAG. This is the #1 applied AI pattern in 2026.

💡 Pro Tip: RAG system design questions appear in almost every applied AI interview. Master this end-to-end.

⚠️ Common Mistake: Not discussing chunking strategy trade-offs (small chunks = precise but lose context; large chunks = opposite).

Real Interview Question: "Design a Q&A system for a company with 1M documents. How do you ensure accurate, cited responses?"

Practice on Thita.ai: Master RAG Systems →

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5. Embeddings & Vector Search (6 Topics)

Foundation: Semantic understanding and similarity at scale

Essential Topics:

• Dense vs Sparse Representations: Learned embeddings vs TF-IDF/BM25, hybrid search combining both
• Embedding Model Architectures: Bi-encoders (fast retrieval), cross-encoders (accurate reranking), ColBERT late interaction
• Dimensionality Trade-offs: 768 vs 1536 dimensions, compression techniques, Matryoshka embeddings
• Similarity Metrics: Cosine similarity, dot product, Euclidean distance — when to use each
• Approximate Nearest Neighbor (ANN): HNSW algorithm, inverted file index (IVF), product quantization (PQ)
• Vector Database Design: Indexing strategies, sharding across nodes, caching hot vectors, query optimization

Why This Matters: Vector search powers semantic search, recommendation systems, and RAG. It's foundational infrastructure.

💡 Pro Tip: Understand the speed vs accuracy trade-off in ANN search. HNSW is fast and accurate but memory-intensive.

Real Interview Question: "Build a semantic code search engine for 10M code snippets. How do you index and query?"

Practice on Thita.ai: Explore Embeddings →

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6. Prompt Engineering & In-Context Learning (7 Topics)

Foundation: Getting LLMs to reliably do what you want

Essential Topics:

• Zero-Shot, Few-Shot, Many-Shot: In-context learning without fine-tuning, example selection strategies
• Chain-of-Thought (CoT) Reasoning: Step-by-step reasoning, zero-shot CoT ("Let's think step by step"), self-consistency
• ReAct Pattern: Reasoning + Acting, interleaving thought and action, tool use integration
• Prompt Templates: Structured prompts, variable injection, DSPy for systematic optimization
• System Prompts & Personas: Instruction hierarchy, role setting, behavioral constraints
• Automatic Prompt Optimization: Gradient-free optimization, DSPy framework, evolutionary strategies
• Structured Output Generation: JSON mode, function calling, grammar-constrained decoding

Hottest Topic in 2026: Prompt engineering is now a first-class engineering discipline, not just trial-and-error.

💡 Pro Tip: Learn DSPy (Stanford) for systematic prompt optimization instead of manual prompt tweaking.

⚠️ Common Mistake: Over-engineering prompts without measuring improvement on held-out test sets.

Real Interview Question: "Design a prompt system for multi-step customer support. How do you ensure consistency and quality?"

Practice on Thita.ai: Master Prompt Engineering →

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7. AI Agents & Tool Use (7 Topics)

Foundation: Autonomous AI systems that reason and take actions

Essential Topics:

• Function Calling Mechanisms: Tool schemas, parameter extraction, OpenAI function calling, Anthropic tool use
• Agent Architectures: ReAct (reason + act), Plan-and-Execute, Reflection agents, Multi-agent systems
• Multi-Step Reasoning: Task decomposition, planning algorithms, forward/backward chaining
• Memory Systems: Short-term (conversation history), long-term memory (vector store), semantic memory
• Tool Libraries: API calling, code execution, web browsing, database queries, calculator tools
• Agent Evaluation: Task success rate, step efficiency, safety violations, tool selection accuracy
• Multi-Agent Coordination: Agent collaboration, delegation, competition, debate-based reasoning

Why This Is Critical: Every AI company is building agents in 2026. This is the fastest-growing interview topic.

💡 Pro Tip: ReAct is the standard pattern. Know it cold: Thought → Action → Observation loop.

Real Interview Question: "Design an AI agent that books flights by searching the web and calling airline APIs. What's your architecture?"

Common Pattern: Questions combine prompt engineering, tool use, and error handling.

Practice on Thita.ai: Build AI Agents →

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8. Fine-Tuning & Model Adaptation (6 Topics)

Foundation: Customizing pre-trained models for specific domains

Essential Topics:

• Full Fine-Tuning: Learning rate strategies, catastrophic forgetting mitigation, curriculum learning
• Parameter-Efficient Fine-Tuning (PEFT): LoRA, QLoRA, Adapters, Prefix Tuning, (IA)³
• Instruction Tuning & Alignment: Supervised fine-tuning (SFT), RLHF, DPO (Direct Preference Optimization), RLAIF
• Few-Shot Learning: In-context learning, meta-learning (MAML), prototypical networks
• Domain Adaptation: Continued pretraining, task-specific adaptation, vocabulary extension
• Model Merging: DARE, TIES, Model Soups, merging multiple fine-tuned models

Why This Matters: Fine-tuning is how you adapt general models (GPT, Llama) to specialized tasks (medical QA, legal analysis).

💡 Pro Tip: LoRA is the production standard in 2026. Understand the low-rank decomposition: W + BA where B, A are low-rank.

⚠️ Common Mistake: Using full fine-tuning when LoRA would be 10x more efficient and prevent overfitting.

Real Interview Question: "You need to adapt Llama 3 70B to medical domain with only 10K examples. What's your approach?"

Practice on Thita.ai: Master Fine-Tuning →

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9. Multimodal AI (6 Topics)

Foundation: Combining vision, audio, and text

Essential Topics:

• Vision Transformers (ViT): Patch embeddings, position encodings, ViT vs CNN trade-offs
• Image-Text Models: CLIP architecture, contrastive learning (InfoNCE loss), zero-shot classification
• Vision-Language Models (VLMs): LLaVA, GPT-4V, Gemini — architecture for image understanding
• Audio-Text Models: Whisper for speech recognition, encoder-decoder for ASR, text-to-speech models
• Cross-Modal Alignment: Shared embedding space, contrastive objectives, temperature scaling
• Multimodal Applications: Image captioning, visual question answering (VQA), text-to-image, video understanding

Growing Field: Multimodal AI is exploding in 2026 with models like GPT-4V, Gemini, and Claude becoming multimodal by default.

💡 Pro Tip: Understand CLIP deeply — it's the foundation for most vision-language models.

Real Interview Question: "Design an image search system where users search with natural language. How do you encode images and text?"

Practice on Thita.ai: Explore Multimodal AI →

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10. GenAI System Design (8 Topics)

Foundation: Building production-grade AI systems at scale

Essential Topics:

• LLM Serving Infrastructure: vLLM, TGI (Text Generation Inference), TensorRT-LLM; model parallelism (tensor, pipeline, data)
• Inference Optimization: Continuous batching, KV cache management, quantization (INT8, INT4), speculative decoding
• Prompt Management Systems: Version control, A/B testing, template management, prompt analytics
• Cost Optimization: Response caching, model routing (small model for simple queries), batch processing
• Latency Optimization: Streaming responses, edge deployment, CDN for model weights, request batching
• Monitoring & Observability: Token usage tracking, latency percentiles (p50, p95, p99), quality metrics, cost dashboards
• Safety Guardrails: Input/output content filtering, PII detection, jailbreak prevention, rate limiting
• ML Data Pipelines: Feature stores, embedding generation pipelines, continuous evaluation loops

Critical for Senior Roles: System design is tested in 90% of L5+ (senior) AI engineering interviews.

💡 Pro Tip: Always discuss trade-offs: latency vs cost vs quality. There's no free lunch.

⚠️ Common Mistake: Ignoring GPU memory constraints. An 8B model needs ~16GB GPU RAM (2 bytes/param for fp16).

Real Interview Question: "Design ChatGPT's backend. How do you handle 10M concurrent users with <2s latency?"

Practice on Thita.ai: Master GenAI System Design →

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11. Model Evaluation & Testing (6 Topics)

Foundation: Measuring what matters in AI systems

Essential Topics:

• LLM Benchmarks: MMLU (knowledge), HumanEval (coding), GSM8K (math), TruthfulQA (truthfulness), BBH
• Generation Metrics: BLEU, ROUGE, METEOR for reference-based; BERTScore for semantic similarity; perplexity
• RAG Evaluation: Faithfulness (no hallucination), answer relevance, context precision, context recall, RAGAS framework
• Human Evaluation: Rating scales, A/B testing, preference ranking (Elo ratings), inter-annotator agreement
• LLM-as-Judge: Using GPT-4 to evaluate outputs, prompt design for evaluation, correlation with human judgments
• Statistical Testing: P-values, confidence intervals, bootstrap sampling, sample size calculations

Why This Is Important: "How do you measure success?" is literally asked in every AI system design interview.

💡 Pro Tip: Know when automated metrics fail: creativity, safety, cultural sensitivity need human evaluation.

⚠️ Common Mistake: Relying only on BLEU/ROUGE for LLM evaluation. These correlate poorly with human judgments.

Real Interview Question: "Your new RAG system launches tomorrow. How do you evaluate if it's better than the baseline?"

Practice on Thita.ai: Learn Evaluation Methods →

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12. AI Safety, Ethics & Bias (6 Topics)

Foundation: Building responsible AI systems

Essential Topics:

• Bias Detection & Mitigation: Fairness metrics (demographic parity, equalized odds), debiasing techniques
• Safety Guardrails: Content moderation, adversarial prompt detection, jailbreak prevention (prefix injection, suffix attacks)
• Privacy & Security: Differential privacy, PII detection and redaction, model inversion attacks, data poisoning
• Explainability & Interpretability: Attention visualization, SHAP values, LIME, feature importance
• Red Teaming: Adversarial testing, finding failure modes, systematic evaluation of safety
• Ethical Deployment: Transparency (model cards), accountability, consent, dual-use concerns

Critical in 2026: AI safety is now table stakes. Anthropic, OpenAI, Google all have dedicated safety teams and test this in interviews.

💡 Pro Tip: Know about Constitutional AI (Anthropic), red teaming practices, and RLHF for alignment.

⚠️ Common Mistake: Treating safety as an afterthought. It should be designed in from day one.

Real Interview Question: "Design an AI hiring assistant. How do you prevent bias against protected groups?"

Practice on Thita.ai: Explore AI Safety →

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13. Open Source & Model Ecosystem (6 Topics)

Foundation: Navigating the open-source AI landscape

Essential Topics:

• Model Selection: Llama 3, Mistral, Gemma, Phi, Qwen, Command R — capabilities, size, speed trade-offs
• Licensing Considerations: Apache 2.0 (commercial-friendly), Llama 3 license (conditionally commercial), research-only licenses
• Model Optimization: Quantization (GPTQ, AWQ, GGUF formats), pruning, knowledge distillation
• Deployment Strategies: Local inference (Ollama, llama.cpp), cloud (AWS SageMaker, Modal), edge/mobile (on-device)
• HuggingFace Ecosystem: Transformers library, Datasets, PEFT, Accelerate, TRL (transformer RL)
• Fine-Tuning Tools: Axolotl, LLaMA Factory, Unsloth, Torchtune — when to use each

Practical Knowledge: 90% of AI startups use open-source models. Know this landscape cold.

💡 Pro Tip: Llama 3 is commercially licensed for companies under $700M revenue. Mistral and Gemma are Apache 2.0 (fully open).

Real Interview Question: "Choose an LLM for a startup chatbot with 10K daily users and limited budget. Justify your choice."

Practice on Thita.ai: Explore Open Source Models →

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Real-World Case Studies: Learning from Production Systems

Studying how top companies built their AI systems is invaluable. Here are 12 production architectures to learn from:

ChatGPT Conversation System

Architecture: Streaming responses, conversation context management, memory across sessions Key Decisions: KV cache for fast inference, truncation strategies for long conversations, user-specific prompt templates Scale: 100M+ weekly active users, sub-2s response time Trade-offs: Quality vs latency (smaller models for simple queries), context length vs cost

GitHub Copilot

Architecture: Code completion with fill-in-the-middle, repository context, fine-tuned Codex Key Decisions: Low-latency inference (<100ms), prefix/suffix context, syntax-aware completions Scale: 1M+ developers, 10M+ completions per day Trade-offs: Model size vs latency, local caching vs API calls

Perplexity Search

Architecture: RAG + web search + citation, real-time web crawling, answer streaming Key Decisions: Hybrid search (keyword + semantic), source attribution, real-time index updates Scale: 10M+ queries per month Trade-offs: Answer speed vs comprehensiveness, citation accuracy vs coverage

Notion AI

Architecture: Multi-tenant LLM serving, workspace context injection, document embeddings Key Decisions: Workspace-specific fine-tuning, incremental context updates, cost per user optimization Scale: 30M+ users, per-workspace isolation Trade-offs: Personalization vs inference cost, privacy vs feature richness

Midjourney Image Generation

Architecture: Diffusion models, Discord bot interface, GPU cluster orchestration Key Decisions: Queue management, progressive generation previews, style transfer Scale: 15M+ users, 1B+ images generated Trade-offs: Quality vs generation time, resolution vs GPU cost

Claude's Constitutional AI

Architecture: RLHF with AI feedback (RLAIF), multi-step refinement, helpfulness/harmlessness balance Key Decisions: Red teaming for safety, constitutional principles, scalable oversight Scale: Production safety at OpenAI scale Trade-offs: Safety vs capabilities, refusal false positives

Meta's Llama Training

Architecture: Multi-node distributed training, data curation pipelines, scaling to 405B parameters Key Decisions: Pretraining data mix, compute-optimal scaling, RoPE positional embeddings Scale: Trained on 15T tokens, thousands of GPUs Trade-offs: Model size vs inference cost, pretraining compute vs data quality

OpenAI Function Calling

Architecture: Structured output generation, tool schema validation, multi-turn tool use Key Decisions: JSON mode, parallel function calling, automatic parameter extraction Scale: Powers ChatGPT plugins and GPTs Trade-offs: Flexibility vs reliability, schema complexity vs usability

Anthropic Prompt Caching

Architecture: Prefix caching for repeated context, cache-aware routing, TTL management Key Decisions: Cache key design, invalidation strategies, cost reduction (90% for cached tokens) Scale: Reduces inference cost for all Claude API users Trade-offs: Cache hit rate vs memory usage, staleness vs freshness

Cohere Enterprise RAG

Architecture: Workspace knowledge bases, connector framework, citation tracking Key Decisions: Multi-source ingestion, access control, grounding score thresholds Scale: Fortune 500 deployments Trade-offs: Accuracy vs coverage, security vs convenience

Hugging Face Inference Endpoints

Architecture: Serverless model serving, auto-scaling, multi-region deployment Key Decisions: Cold start optimization, model caching, pay-per-token pricing Scale: 100K+ models served Trade-offs: Flexibility vs cost efficiency, cold start latency vs idle cost

Runway Gen-3 Video Generation

Architecture: Temporal consistency models, frame interpolation, progressive refinement Key Decisions: Latent diffusion, motion control, resolution scaling Scale: Professional video creation tool Trade-offs: Video length vs generation time, quality vs rendering cost

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How to Learn AI/LLMs Effectively with Thita.ai

Knowing the roadmap is just the start. Deliberate practice with real-time feedback is what converts knowledge into interview success and production capability.

Here's how Thita.ai's AI-powered platform accelerates your AI/LLM mastery:

1. Follow the Structured Learning Path

Visit Thita.ai's AI Learning Path to see all 13 categories with 80+ topics, organized exactly as outlined in this roadmap. Progress through topics systematically with curated problems.

2. Solve Real AI Problems

Access 260+ AI/ML interview problems covering:

• Neural network design and optimization
• Transformer architecture questions
• RAG system design challenges
• Agent implementation problems
• Production system design scenarios
• Safety and evaluation questions

3. Get AI Coaching in Real-Time

• Ask the AI coach to explain any concept from the roadmap
• Discuss trade-offs: "Should I use full fine-tuning or LoRA?"
• Get hints when stuck on problems
• Understand multiple solution approaches
• Learn at your own pace with personalized guidance

4. Practice AI/LLM Interviews

• Simulate real interviews with an AI interviewer
• Get evaluated on technical depth, communication, and problem-solving
• Receive detailed feedback on your architecture designs
• Practice explaining complex concepts clearly
• Build confidence before real interviews

Pro Tip: Use Interview Mode for realistic practice, then switch to Learn Mode for deep conceptual exploration.

Start Practicing AI Interviews →

5. Track Your Progress

• See your mastery across all 13 categories
• Identify knowledge gaps and focus study time
• Build momentum with consistent learning
• Monitor improvement over time
• Get personalized recommendations on what to study next

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Your 12-Week Action Plan: From Zero to AI Engineering Mastery

Don't just read — take action with this structured 12-week plan:

Weeks 1-2: Foundations

Focus: Neural Network Fundamentals, Transformer Architecture

Master the mathematical foundations before diving into LLMs.

Topics to Cover:

• Backpropagation and gradient descent
• Activation and loss functions
• Self-attention mechanism
• Multi-head attention
• Positional encodings

Practice Tasks:

• Implement attention from scratch in NumPy
• Explain why transformers replaced RNNs
• Derive attention gradients
• Compare pre-norm vs post-norm

Goal: Solid mathematical foundation for all future topics

Start with Foundations →

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Weeks 3-4: LLM Mastery

Focus: Large Language Models, Tokenization, Decoding

Understand how GPT, Claude, and Llama actually work internally.

Topics to Cover:

• Causal language modeling objective
• BPE and SentencePiece tokenization
• Sampling strategies (nucleus, top-k, temperature)
• KV cache optimization
• Context window management

Practice Tasks:

• Implement nucleus sampling
• Analyze tokenization of different languages
• Compare greedy vs sampling for different tasks
• Design a long-context strategy for 1M token documents

Goal: Deep understanding of LLM internals

Master LLMs →

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Weeks 5-6: Applied LLM Patterns

Focus: RAG Systems, Embeddings, Vector Search

Build the most common production LLM pattern.

Topics to Cover:

• Dense retrieval with embeddings
• Vector database indexing (HNSW, IVF)
• Document chunking strategies
• Reranking techniques
• RAG evaluation metrics

Practice Tasks:

• Build an end-to-end RAG system
• Optimize chunking for different document types
• Implement reranking with a cross-encoder
• Measure faithfulness and relevance

Goal: Production-ready RAG knowledge

Build RAG Systems →

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Weeks 7-8: Advanced Techniques

Focus: Prompt Engineering, AI Agents, Fine-Tuning

Master advanced patterns for production systems.

Topics to Cover:

• Chain-of-thought and ReAct patterns
• Function calling and tool use
• Agent architectures
• LoRA fine-tuning
• Instruction tuning

Practice Tasks:

• Design a ReAct agent for web search
• Implement LoRA from scratch
• Create a multi-step reasoning prompt
• Fine-tune Llama 3 for domain-specific QA

Goal: Advanced pattern mastery

Explore Advanced Patterns →

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Weeks 9-10: Multimodal & System Design

Focus: Multimodal AI, GenAI System Design

Scale systems to production and handle multiple modalities.

Topics to Cover:

• Vision transformers (ViT)
• CLIP architecture
• LLM serving infrastructure (vLLM)
• Inference optimization
• Cost and latency trade-offs

Practice Tasks:

• Design an image search system with CLIP
• Architect LLM serving for 10M users
• Optimize inference latency to <100ms
• Calculate cost per 1M tokens

Goal: System design readiness for senior interviews

Master System Design →

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Weeks 11-12: Evaluation, Safety & Interview Prep

Focus: Model Evaluation, AI Safety, Real-World Case Studies

Prepare for interviews with holistic understanding.

Topics to Cover:

• LLM benchmarks (MMLU, HumanEval)
• RAG evaluation with RAGAS
• Bias detection and mitigation
• Safety guardrails
• Red teaming

Practice Tasks:

• Design an evaluation framework for a chatbot
• Implement bias detection in a hiring AI
• Study all 12 case studies deeply
• Do 10+ mock interviews on Thita.ai
• Review common interview questions

Goal: Interview confidence and production readiness

Practice Mock Interviews →

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Pro Tips from AI Engineers at Top Companies

From an OpenAI ML Engineer:

"Don't just learn to use APIs. Understand transformers from first principles. We ask candidates to derive attention mechanisms and explain optimization trade-offs. Surface-level knowledge doesn't cut it."

From an Anthropic Safety Researcher:

"In 2026, safety and alignment are first-class concerns, not afterthoughts. Every interview at Anthropic includes questions about RLHF, red teaming, and preventing jailbreaks. Study Constitutional AI."

From a Meta AI Research Scientist:

"Open-source models like Llama 3 are production-ready and power real products. Know how to deploy them efficiently. We test practical engineering: quantization, distributed serving, cost optimization."

From a Google DeepMind Engineer:

"RAG is table stakes now. The frontier is multi-agent systems, planning, and tool use. Study ReAct, plan-and-execute architectures, and how agents coordinate. That's where the field is heading."

From a Cohere Applied ML Lead:

"Evaluation is everything. 'How do you measure success?' comes up in every interview. Know LLM-as-judge, RAGAS for RAG, and when to use human evaluation vs automated metrics."

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Common Mistakes to Avoid

1. Starting with Agents Before Understanding LLMs Don't build agents if you don't deeply understand transformers, prompting, and tool use. Master foundations first.

2. Ignoring RAG Evaluation Metrics "I'll use RAG" isn't enough. Discuss faithfulness, relevance, chunking strategy, and how you'd measure improvement.

3. Over-Engineering Prompts Without Testing Random prompt tweaking wastes time. Use systematic evaluation on test sets. Consider DSPy for optimization.

4. Not Considering Cost and Latency Every design decision has cost/latency implications. A 70B model costs 10x more than a 7B model per token.

5. Treating Safety as an Afterthought AI safety should be designed in from day one. Know content filtering, jailbreak prevention, and bias mitigation.

6. Only Knowing OpenAI APIs Understanding how to call GPT-4 API isn't enough. Know transformer architecture, fine-tuning, RAG, and open-source models.

7. Memorizing Without Understanding Don't memorize attention formulas. Understand why scaled dot-product prevents vanishing gradients. Depth matters.

8. Skipping Fundamentals You can't truly understand LLMs without solid neural network foundations. Don't skip Weeks 1-2.

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Commonly Asked AI Interview Questions

Here are 20 real interview questions from Google, Meta, OpenAI, Anthropic, and other top AI companies:

Architecture & Fundamentals

1. Explain the transformer architecture. Why is self-attention better than RNNs?
2. Derive the attention mechanism from first principles. Why divide by √d_k?
3. What's the difference between BERT and GPT architectures? When to use each?
4. Explain how positional encodings work in transformers.
5. Compare multi-head attention to single-head attention. Why multiple heads?

Production Systems

6. Design a RAG system for customer support with 100K documents. How do you ensure accuracy?
7. You need to serve an LLM with <100ms latency. What optimizations do you apply?
8. Design ChatGPT's backend. How do you handle 10M concurrent users?
9. Build a semantic code search engine for 10M code snippets.
10. Design an AI coding assistant like GitHub Copilot.

Advanced Techniques

11. Design an AI agent that books flights using web search and APIs.
12. How would you fine-tune Llama 3 70B to medical domain with limited data?
13. Explain LoRA. Why is it more efficient than full fine-tuning?
14. Design a multi-agent system where agents collaborate to solve research tasks.
15. Implement ReAct pattern for a web search agent.

Evaluation & Safety

16. How do you evaluate whether your new RAG system is better than baseline?
17. Design an AI hiring assistant. How do you prevent bias?
18. What metrics would you use to evaluate a creative writing AI?
19. Explain RLHF. How does it improve LLM alignment?
20. How do you prevent jailbreak attacks on a production LLM?

Practice these on Thita.ai with AI interviewer feedback!

Start Mock Interviews →

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Conclusion & Next Steps

Stop the scattered learning — start with structure.

These 80+ AI/LLM topics (organized into 13 core categories, plus 12 real-world case studies and 20 practice problems) are your complete roadmap to mastering AI engineering in 2026.

The reality: Most aspiring AI engineers waste months watching random YouTube tutorials and reading scattered blog posts without a clear path. The top 1% follow a structured roadmap and practice with feedback.

Which group will you join?

Start your journey today:

1. Open Thita.ai's AI Learning Path → — Follow the structured curriculum with 260+ problems
2. Practice with AI Coach → — Get personalized explanations and guidance
3. Do Mock AI Interviews → — Simulate real interviews and get detailed feedback
4. Follow the 12-Week Plan — Structured learning from foundations to mastery

Your next AI engineering interview will feel like a roadmap you've already traveled.

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Ready to Master AI/LLM Engineering?

Join thousands of engineers who've landed offers at OpenAI, Anthropic, Google, Meta, and more using Thita.ai's structured, AI-powered approach.

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Last Updated: September 4, 2026

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External Resources

• Thita.ai AI Learning Path
• Thita.ai AI Mock Interviews
• Thita.ai AI Coach
• OpenAI Research
• Anthropic Research
• HuggingFace Hub

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