#Reasoning Models

Articles about Reasoning Models — exploring patterns, best practices, and real-world implementations in production systems.

8 posts tagged with reasoning models. ← All posts

#A2A (14)#ADK (8)#AG-UI (6)#AI (9)#AI Agents (311)#AI Architecture (10)#AI Cost (10)#AI Cost Optimization (8)#AI Engineering (201)#AI Evaluation (8)#AI Governance (29)#AI Red Teaming (9)#AI Research (9)#AI Safety (8)#AI Security (21)#AI in Production (12)#AML (3)#API Design (10)#API Security (8)#APIs (55)#AWS (17)#Accounting (9)#Agent Skills (3)#Agentic AI (24)#Agentic Commerce (12)#Agentic RAG (8)#Agents (4)#Amazon Bedrock (8)#Analytics (3)#Architecture (36)#Audit (3)#Authentication (11)#Authorization (3)#Automation (6)#Azure (11)#Azure AI Foundry (9)#Backend Engineering (310)#Benchmarks (3)#Best Practices (3)#BigQuery (6)#Business Finance (8)#Business Strategy (55)#C (8)#CI/CD (8)#Caching (10)#Capital Markets (14)#Card Payments (12)#Cards (13)#Career (24)#Checkpointing (4)#Claude Code (8)#Cloud (5)#Cloud Architecture (3)#Cloud Native (10)#Code Review (8)#Collaboration (5)#Communication (9)#Compliance (52)#Computer Networking (9)#Computer Science (32)#Computer Vision (5)#Concurrency (37)#Consulting (3)#Containers (10)#Context Engineering (10)#Cost Optimisation (5)#Credit (14)#Credit Risk (14)#CrewAI (8)#Crypto (12)#Cryptocurrency (12)#Cryptography (8)#Custody (9)#DSPy (8)#Data (13)#Data Engineering (12)#Data Structures (9)#Databases (37)#Deployment (3)#Design Patterns (10)#DevOps (16)#DevSecOps (11)#Developer Experience (5)#Developer Tools (5)#Distributed Systems (94)#Documentation (3)#Edge AI (8)#Embeddings (15)#Emotional Intelligence (8)#Energy (8)#Engineering (11)#Engineering Culture (3)#Engineering Practices (16)#Error Handling (4)#Evaluation (53)#Event-Driven Architecture (8)#FREE-AI (8)#FX (5)#Feedback (4)#FinOps (23)#FinTech (6)#Financial AI (14)#Financial Systems (129)#Fine-Tuning (11)#Fintech (131)#Flutter (8)#Foreign Exchange (5)#Forward Deployed Engineer (8)#Forward Deployed Engineering (8)#Fraud (10)#Function Tools (5)#Functional Programming (3)#Fundraising (8)#GCP (5)#Gemma (4)#Generative AI (3)#Git (8)#Go (220)#Go-to-Market (8)#Google ADK (36)#Governance (56)#Granite (6)#GraphQL (3)#Growth (3)#Guardrails (25)#HIPAA (3)#HTTP (3)#Harness Engineering (8)#Hiring (8)#Hugging Face (8)#Human-in-the-Loop (8)#IBM watsonx (8)#Identity (11)#Integration (3)#Intellectual Property (8)#Interfaces (3)#JavaScript (8)#KYC (11)#KYC and AML (12)#Kafka (10)#Kubernetes (17)#LLM (5)#LLM Inference (8)#LLM-as-Judge (3)#LLMs (135)#LangChain (8)#LangGraph (11)#Leadership (26)#Ledger (12)#Legal (8)#Lending (14)#LlamaIndex (8)#MCP (22)#MLOps (31)#Machine Learning (32)#Marketing (16)#Markets (4)#Memory (15)#Memory Management (5)#Metrics (6)#Microservices (3)#Microsoft Agent Framework (150)#Middleware (6)#Migration (9)#Monitoring (3)#Multi-Agent (10)#Multi-Agent AI (14)#Multi-Agent Systems (73)#Multimodal (3)#Multimodal AI (8)#NIM (5)#NVIDIA (8)#OAuth (3)#OWASP (7)#Observability (44)#On-Device AI (8)#Open Source (7)#OpenTelemetry (5)#Operating Systems (8)#Operations (10)#Opinion (6)#Orchestration (10)#Organizational Design (8)#Payment Rails (16)#Payments (54)#People (8)#Performance (43)#Platform Engineering (9)#PreSales (8)#Privacy (5)#Privacy Engineering (3)#Process (4)#Product (27)#Product Management (8)#Production (8)#Programming (10)#Programming Languages (48)#Prompt Engineering (66)#Prompt Injection (6)#Protocol Buffers (3)#Protocols (9)#Providers (4)#Pydantic AI (8)#Python (142)#Quality (3)#RAG (58)#RBI (3)#REST (5)#Rails (16)#Reasoning Models (8)#Reconciliation (3)#RegTech (8)#Regulation (9)#Reliability (48)#Resilience (4)#Responsible AI (5)#Retrieval (3)#Risk (13)#Rust (32)#SRE (21)#Sales (9)#Scalability (3)#Security (81)#Security Engineering (8)#Self-Evolving Agents (16)#Sessions (3)#Settlement (9)#Soft Skills (8)#Software (3)#Software Architecture (36)#Software Engineering (144)#Spanner (4)#Startups (30)#Strands (8)#Streaming (28)#Structured Output (4)#Sustainability (8)#System Design (32)#Systems Programming (48)#Testing (49)#Tool Use (22)#Tooling (4)#Tools (3)#Trading (8)#Treasury (6)#Type System (3)#Type Systems (11)#TypeScript (8)#Vector Databases (21)#Vector Search (11)#Venture Capital (8)#Version Control (8)#Web Development (6)#Workflows (14)#gRPC (13)#smolagents (8)
Pratik Dhanave · ·8 min read

Limits and the Frontier

Reasoning models are a genuine advance — and it's precisely because they're so impressive that their limits are worth stating plainly. A model that writes out careful, step-by-step reasoning invites you to trust the reasoning, and that trust is not always warranted. The chain of thought may not be why the model actually answered. More thinking eventually stops helping. And some problems no amount of test-time compute will solve. Knowing the edges is part of using the capability wisely.

Reasoning models are a genuine advance — and precisely because they're so impressive, their limits are worth stating plainly. The chain of thought may not be why the model actually answered. More thinking eventually stops helping. And some problems no amount of test-time compute will solve.

#Reasoning Models#LLMs#AI Engineering#AI Safety#Testing
Pratik Dhanave · ·7 min read

Using Reasoning Models Well

The habits that made you good at prompting standard models can actively hurt you with reasoning models. "Let's think step by step" is redundant — even counterproductive — when the model already thinks natively. Few-shot examples can constrain reasoning that's better left free. The shift from standard to reasoning models isn't just picking a different model; it's unlearning some prompting reflexes and learning where deep thinking actually earns its cost.

The habits that made you good at prompting standard models can actively hurt you with reasoning models. 'Let's think step by step' is redundant when the model already thinks natively. The shift isn't just picking a different model — it's unlearning some prompting reflexes and learning where deep thinking earns its cost.

#Reasoning Models#LLMs#AI Engineering#Prompt Engineering#Testing
Pratik Dhanave · ·7 min read

The Economics of Thinking

Test-time compute reframes a question engineers rarely had to ask before: how much is a correct answer worth? Because thinking now costs money and time in direct proportion to how much of it you do, reasoning is no longer free — it's a purchase. A reasoning model can generate many times more tokens working through a problem than a standard model uses to answer it, and you pay for every one. Deciding when that's worth it is the core practical skill of the reasoning era.

Test-time compute reframes a question engineers rarely had to ask: how much is a correct answer worth? Thinking now costs money and time in direct proportion to how much of it you do. A reasoning model can generate many times more tokens than a standard model — and you pay for every one.

#Reasoning Models#LLMs#AI Engineering#Cost Optimization#Testing
Pratik Dhanave · ·7 min read

Inference-Time Techniques: Spending Compute for Accuracy

A reasoning model thinks harder on its own — but you can spend test-time compute on top of any model, reasoning or not, to squeeze out more accuracy. Generate many answers and vote. Generate many and pick the best with a verifier. Search a tree of reasoning steps, pruning the bad branches. These techniques share one shape — do more work at inference, then choose well — and understanding them turns test-time compute from a model property into a toolkit you control.

A reasoning model thinks harder on its own — but you can spend test-time compute on top of any model to squeeze out more accuracy. Generate many answers and vote. Generate many and pick the best with a verifier. Search a tree of reasoning steps. These techniques share one shape: do more work, then choose well.

#Reasoning Models#LLMs#AI Engineering#Machine Learning#Testing
Pratik Dhanave · ·8 min read

How Reasoning Models Are Trained

You can't teach deep reasoning by showing a model more examples of good reasoning — because the best reasoning for a hard problem often isn't in any dataset, and imitation caps a model at the quality of what it imitates. The breakthrough behind modern reasoning models was to stop imitating and start rewarding: let the model try to solve problems, check whether it got them right, and reinforce whatever thinking led to correct answers. That shift — from imitation to reinforcement on verifiable outcomes — is why reasoning models can think in ways no one wrote down.

You can't teach deep reasoning by showing more examples — imitation caps a model at what it imitates. The breakthrough was to stop imitating and start rewarding: let the model try to solve problems, check whether it got them right, and reinforce whatever thinking led to correct answers.

#Reasoning Models#Reinforcement Learning#AI Engineering#Machine Learning#LLMs#Testing
Pratik Dhanave · ·7 min read

Test-Time Compute: The New Scaling Axis

The dominant story of AI progress for years was training-time scale: bigger models, more data, more training compute. Test-time compute is a second, independent axis — spend more computation when you run the model, not when you train it, and get better answers on hard problems. It reframes a trained model not as a fixed-capability artifact but as one whose performance you can dial up per query by letting it think more.

The dominant story of AI progress was training-time scale: bigger models, more data. Test-time compute is a second, independent axis — spend more computation when you *run* the model, not when you *train* it, and get better answers on hard problems. It reframes a model as one whose performance you can dial up per query.

#Reasoning Models#LLMs#AI Engineering#Machine Learning#Testing
Pratik Dhanave · ·7 min read

Chain of Thought: The Idea That Started It

The observation that launched reasoning models was almost embarrassingly simple: if you ask a language model to "think step by step" before answering, it gets dramatically better at hard problems — with no change to the model at all. That a few words of prompting could unlock reasoning already latent in the model was a clue about something deep: the ability to reason was there, waiting to be elicited, and giving the model room to work was the key.

The observation that launched reasoning models was almost embarrassingly simple: ask a model to 'think step by step' and it gets dramatically better at hard problems, with no change to the model. That a few words could unlock latent reasoning was a clue about something deep.

#Reasoning Models#LLMs#AI Engineering#Prompt Engineering#Testing
Pratik Dhanave · ·7 min read

What Reasoning Models Are

For years, the way to make a language model better was to make it bigger — more parameters, more training data. Reasoning models represent a different bet: instead of only scaling what the model knows, scale how much it thinks before answering. A reasoning model spends extra computation at inference time working through a problem step by step before committing to an answer — and on hard problems, that changes what's possible.

For years, making a language model better meant making it bigger. Reasoning models represent a different bet: scale how much the model *thinks* before answering, not just what it knows. A reasoning model spends extra computation working through a problem step by step — and on hard problems, that changes what's possible.

#Reasoning Models#LLMs#AI Engineering#Machine Learning#Testing

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.