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.
Retrieval Caching and Freshness: Faster Answers Without Stale Facts
A deep dive into caching strategies for retrieval systems that preserve speed without sacrificing freshness.
AI Inference Optimization Stack: Latency, Cost, and Quality
A production-focused guide to optimizing AI inference with batching, caching, quantization, and routing strategies.
Data-Centric LLM Iteration: Improving Models Without Bigger Architectures
Why high-quality data, labeling strategy, and error analysis often beat model scaling in production.
Fine-Tuning vs. Instruction Tuning: What Actually Improves LLMs
A clear comparison of fine-tuning, instruction tuning, and alignment, with guidance on when each approach makes sense.
Knowledge Distillation for Inference: Smaller Models, Real Speed
A deep dive into distillation pipelines that preserve quality while cutting inference cost.
Vector Databases and Embeddings: A Practical Engineering Guide
How embeddings are created, stored, and retrieved in vector databases, with real-world design choices for speed and relevance.
Structured Output and Schema Guards: Making LLMs Deterministic
How to enforce structured outputs with schemas, validators, and constrained decoding for production reliability.
LLM Guardrails and Safety Layers: Practical Patterns for Real Products
A hands-on guide to building guardrails, moderation layers, and policy enforcement for LLM-powered applications.
Temporal Reasoning and Time Awareness in LLM Systems
How to design LLM systems that reason over time, handle recency, and avoid stale conclusions.
Prompt Systems, Not Prompt Tricks: A Production-Ready Approach
How to move from ad-hoc prompts to robust prompt systems with templates, guardrails, and evaluation loops.
Prompt Robustness and Adversarial Testing: Hardening LLM Interfaces
A deep dive into adversarial prompt testing, robustness metrics, and systematic hardening of LLM inputs.
Transformers vs. Mixture of Experts: When to Use Each Architecture
A practical comparison of dense transformers and MoE models, focusing on cost, latency, and real-world deployment trade-offs.
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