Language AI Handbook

Tokenization, normalization, and cleaning techniques for natural language text

From Word2Vec to GloVe: understanding distributed representations of words

Classical text representation methods and their applications

Identifying and classifying named entities in text

Linguistic annotation and grammatical analysis of text

Recurrent neural networks for sequence modeling and language understanding

Gated recurrent units and bidirectional processing for improved performance

Understanding attention in neural networks and its role in language processing

Encoder-decoder architectures for machine translation and text generation

The mathematical foundations of self-attention and its computational efficiency

Parallel attention mechanisms for capturing different types of relationships

Injecting positional information into transformer models

RMSNorm vs. LayerNorm, residual scaling, μParam/DeepNet-style tweaks for depth and stability

Position-wise feed-forward layers in transformer architecture

AdamW vs. Adafactor/LAMB, learning rate schedules (warmup, cosine), gradient clipping, weight decay, label smoothing, initialization & stability checks

Transformer-XL, Reformer/Performer, BigBird, attention sliding windows, memory/k-NN augmentations, practical context extension (RoPE scaling/ALiBi)

Understanding BERT's bidirectional training and masked language modeling

Autoregressive language modeling and text generation capabilities

Improvements and optimizations to BERT architecture

Unified text-to-text framework for all NLP tasks

Efficient pre-training with replaced token detection

Web-scale corpus building, deduplication (MinHash/SimHash), language ID, quality filtering, document boundaries, license & PII handling, contamination checks, dataset versioning

Scaling laws, few-shot learning, and emergent capabilities in large models

Google's pathway language models and their architectural innovations

Meta's efficient large language models and open-source alternatives

Constitutional AI and safety-focused language model development

Understanding how capabilities emerge with model size

LoRA, AdaLoRA, and other efficient adaptation methods

Training models to follow instructions and human preferences

Aligning language models with human values and preferences

Adapting pre-trained models to specific domains and use cases

Cross-lingual transfer and multilingual language understanding

Knowledge distillation, pruning/sparsity, low-rank factorization, adapter fusion/merging—trade-offs for on-device or low-latency use

Catastrophic forgetting, EWC/regularization, rehearsal/buffers, evaluation protocols for retention vs. adaptation

Large language models for programming and software development

Combining language models with external knowledge sources

Retriever training (Contriever/DPR), hard-negative mining, cross-encoder reranking, hybrid BM25+dense, HNSW/IVF index tuning, chunking/windowing strategies, freshness & caching; attributed QA metrics

Enabling language models to interact with external tools and APIs

Vision-language models and multimodal understanding

ASR/TTS, streaming decoders, speech-LLMs; useful for conversational AI with voice

Building chatbots and conversational agents with language models

Benchmarks, metrics, and evaluation frameworks for language models

Identifying and mitigating bias in language models

Understanding and addressing hallucination in language models

Ensuring language models are safe, helpful, and aligned with human values

Prompt-injection defenses, tool/RAG sandboxing, PII detection/redaction, membership-inference risks, differential privacy (DP-SGD) overview, watermarking/provenance

Understanding what language models learn and how they make decisions

Sparse autoencoders, circuit/feature probing, steering/activation editing—clearly marked as advanced/optional

Experiment tracking, eval regression suites, canary/shadow deploys, rollback plans, incident response, monitoring drift & safety regressions

Pipelines, quality filtering, licensing, and contamination prevention

AdamW/Adafactor, learning rate policies, and stability techniques

FSDP/ZeRO, AMP, checkpointing, FlashAttention, and scalable training systems

KV cache, quantization, batching, speculative decoding, and production serving

Eval regression, canary/shadow deployments, and incident response