8bit.tr Journal
Ideas, frameworks, and playbooks for modern product teams.
Clear, practical articles about building digital products that people love. Short, useful, and built for teams that ship.
Safety Policy Orchestration: Enforcing Rules Across LLM Pipelines
A practical architecture for enforcing safety policies across prompts, tools, and output layers.
Hallucination Mitigation Systems: Engineering for Factuality
A systems-level approach to reducing hallucinations using retrieval, verification, and structured generation.
Governed Knowledge Bases: Trust, Versioning, and Access Control
A framework for building governed knowledge bases with provenance, versioning, and access control.
Synthetic Data for LLMs: Quality, Diversity, and Safety
How to generate synthetic data that improves model performance without amplifying bias or noise.
LLM Latency Profiling and Optimization: Finding the Real Bottlenecks
How to profile LLM latency end-to-end and optimize the slowest paths in production.
KV Cache and Attention Optimization: The Hidden Performance Layer
A deep technical guide to KV caching, attention optimization, and memory-aware serving for LLMs.
Hierarchical Retrieval and Chunking: Scaling Knowledge Without Noise
A technical guide to hierarchical retrieval, chunking strategies, and multi-stage evidence selection.
LLM Data Pipeline Design: From Collection to Continuous Refresh
Engineering a reliable data pipeline for LLMs, including sourcing, filtering, deduplication, and ongoing refresh strategies.
Context Window Allocation: Budgeting Tokens for Maximum Signal
How to allocate context windows across system prompts, memory, and retrieval to maximize model performance.
RLHF and Preference Optimization: Aligning LLMs With Real Users
A deep dive into RLHF pipelines, preference data, and practical alignment strategies for production LLMs.
LLM Observability and Tracing: Seeing What the Model Actually Did
A practical guide to tracing, logging, and debugging LLM workflows in production systems.
Causal Reasoning for LLM Systems: From Correlation to Control
A technical guide to causal reasoning in AI systems, with practical patterns for reducing spurious correlations in LLM workflows.
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