The Future Of Software engineering and AI: What YOU can do about it
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Lessons
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Hard-negative mining strategiesAI bootcamp 2- Implement pipelines that automatically surface confusing negatives
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Cohere Rerank API & SBERT fine-tuning ([sbert.net], Hugging Face)AI bootcamp 2- Learn to use off-the-shelf rerankers like Cohere’s API or fine-tune SBERT models to optimize document ranking post-retrieval
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Triplet-loss fundamentals and semi-hard negative miningAI bootcamp 2- Dive into triplet formation strategies - Focusing on how to find semi-hard negatives (similar but incorrect results that challenge the model)
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Tri-encoder vs cross-encoder performance trade-offsAI bootcamp 2- Explore the architectural trade-offs between Bi/tri-encoders vs cross-encoders - Learn when to use hybrid systems (e.g., bi-encoder retrieval + cross-encoder reranking)
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Contrastive loss vs triplet lossAI bootcamp 2- Compare the two core objectives used for fine-tuning retrievers - Understand how each behaves in hard-negative-rich domains like code or finance
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Query routing logic and memory-index hybridsAI bootcamp 2- Implement index routing systems where queries are conditionally routed: - short factual query → lexical index - long reasoning query → dense retriever - visual question → image embedding index - Learn how to fuse local memory with global vector stores for agentic long-term retrieval
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Multi-vector DB vs standard DBAI bootcamp 2- Understand how multi-vector databases (e.g., ColBERT, Turbopuffer) store multiple vectors per document to support fine-grained relevance - Contrast this with standard single-vector-per-doc retrieval (e.g., FAISS), and learn when multi-vector setups are worth the extra complexity
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Late interaction methods (ColQwen-Omni, audio+image chunks)AI bootcamp 2- Study late interaction architectures (like ColQwen-Omni) that separate dense retrieval from deep semantic fusion - Explore how these models support chunking and retrieval over image, audio, and video-text combinations using attention-based fusion at scoring time
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Cartridge-based retrieval (self-study distillation)AI bootcamp 2- Learn how to modularize retrieval into topic- or task-specific “cartridges.” - Understand that cartridges are pre-distilled context sets for self-querying agents - Study how this approach is inspired by OpenAI’s retrieval plugin and LangChain’s retriever routers - See how cartridges improve retrieval precision by narrowing memory to high-relevance windows
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RL in decoding, CoT prompting, and feedback loopsAI bootcamp 2- Understand how RL ideas are used without training by introducing dynamic feedback in inference - Apply reward scoring or confidence thresholds to adjust CoT (Chain-of-Thought) reasoning steps - Use external tools (e.g., validators or search APIs) as part of a feedback loop that rewards correct or complete answers - Understand how RL concepts power speculative decoding verification, scratchpad agents, and dynamic rerouting during generation
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Q-learning & Policy Gradients (conceptual overview)AI bootcamp 2- Learn the concept of Q-learning as a method to estimate how good an action (token) is in a specific context (prompt state) - Learn the concept of Policy gradients as a method to directly optimize the probability distribution over actions to maximize long-term reward - Understand how Q-learning and Policy gradients form the basis of RLHF, DPO, and advanced training techniques for aligning LLM behavior
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Monte Carlo vs Temporal Difference (TD) learningAI bootcamp 2- Explore the Monte Carlo and TD methods of learning from sequences