diff --git a/README.md b/README.md
index c406927..7b2f2da 100644
--- a/README.md
+++ b/README.md
@@ -33,12 +33,10 @@ Abstract: *The current discourse around Large Language Models (LLMs) tends to fo
 [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
 [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC-BY--NC--SA-4.0-lightgrey.svg
 
-```
-@misc{tharsistpsouza2024tamingllms,
+<!-- @misc{tharsistpsouza2024tamingllms,
   author = {Tharsis T. P. Souza},
   title = {Taming LLMs: A Practical Guide to LLM Pitfalls with Open Source Software},
   year = {2024},
   journal = {GitHub repository},
   url = {https://github.com/souzatharsis/tamingLLMs)
-}
-```
+} -->
diff --git a/tamingllms/_build/.doctrees/environment.pickle b/tamingllms/_build/.doctrees/environment.pickle
index bd3a5eb..a43dd7f 100644
Binary files a/tamingllms/_build/.doctrees/environment.pickle and b/tamingllms/_build/.doctrees/environment.pickle differ
diff --git a/tamingllms/_build/.doctrees/markdown/intro.doctree b/tamingllms/_build/.doctrees/markdown/intro.doctree
index fb094c2..d9ced97 100644
Binary files a/tamingllms/_build/.doctrees/markdown/intro.doctree and b/tamingllms/_build/.doctrees/markdown/intro.doctree differ
diff --git a/tamingllms/_build/html/_sources/markdown/intro.md b/tamingllms/_build/html/_sources/markdown/intro.md
index ab10fe5..19f8b41 100644
--- a/tamingllms/_build/html/_sources/markdown/intro.md
+++ b/tamingllms/_build/html/_sources/markdown/intro.md
@@ -172,10 +172,3 @@ Never share your `.env` file or commit it to version control. It contains sensit
 - Check the book's repository issues page for known problems and solutions
 
 Now that your environment is set up, let's begin our exploration of LLM challenges.
-
-## About the Author
-
-Tharsis Souza (Ph.D. Computer Science, UCL University of London) is a computer scientist and product leader specializing in AI-based products. He is a Lecturer at Columbia University's Master of Science program in Applied Analytics, (*incoming*) Head of Product, Equities at Citadel, and former Senior VP at Two Sigma Investments. He mentors under-represented students & working professionals to help create a more diverse global AI ecosystem.
-
-With over 15 years of experience delivering technology products across startups and Fortune 500 companies, he is also an author of numerous scholarly publications and a frequent speaker at academic and business conferences. Grounded on academic background and drawing from practical experience building and scaling up products powered by language models at early-stage startups, major institutions as well as contributing to open source projects, he brings a unique perspective on bridging the gap between LLMs promised potential and their practical implementation challenges to enable the next generation of AI-powered products.
-
diff --git a/tamingllms/_build/html/markdown/intro.html b/tamingllms/_build/html/markdown/intro.html
index edec3e0..3e95569 100644
--- a/tamingllms/_build/html/markdown/intro.html
+++ b/tamingllms/_build/html/markdown/intro.html
@@ -157,8 +157,6 @@
                 
                   <li class="toctree-l2"><a href="#setting-up-your-environment" class="reference internal">Setting Up Your Environment</a></li>
                 
-                  <li class="toctree-l2"><a href="#about-the-author" class="reference internal">About the Author</a></li>
-                
               </ul>
             
           </li>
@@ -285,7 +283,6 @@
 <li><p><a class="reference internal" href="#troubleshooting-common-issues" id="id14">Troubleshooting Common Issues</a></p></li>
 </ul>
 </li>
-<li><p><a class="reference internal" href="#about-the-author" id="id15">About the Author</a></p></li>
 </ul>
 </li>
 </ul>
@@ -443,11 +440,6 @@ <h3><a class="toc-backref" href="#id14" role="doc-backlink"><span class="section
 <p>Now that your environment is set up, let’s begin our exploration of LLM challenges.</p>
 </section>
 </section>
-<section id="about-the-author">
-<h2><a class="toc-backref" href="#id15" role="doc-backlink"><span class="section-number">2.10. </span>About the Author</a><a class="headerlink" href="#about-the-author" title="Permalink to this heading">¶</a></h2>
-<p>Tharsis Souza (Ph.D. Computer Science, UCL University of London) is a computer scientist and product leader specializing in AI-based products. He is a Lecturer at Columbia University’s Master of Science program in Applied Analytics, (<em>incoming</em>) Head of Product, Equities at Citadel, and former Senior VP at Two Sigma Investments. He mentors under-represented students &amp; working professionals to help create a more diverse global AI ecosystem.</p>
-<p>With over 15 years of experience delivering technology products across startups and Fortune 500 companies, he is also an author of numerous scholarly publications and a frequent speaker at academic and business conferences. Grounded on academic background and drawing from practical experience building and scaling up products powered by language models at early-stage startups, major institutions as well as contributing to open source projects, he brings a unique perspective on bridging the gap between LLMs promised potential and their practical implementation challenges to enable the next generation of AI-powered products.</p>
-</section>
 </section>
 
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diff --git a/tamingllms/_build/html/searchindex.js b/tamingllms/_build/html/searchindex.js
index e614f6e..dfd16a9 100644
--- a/tamingllms/_build/html/searchindex.js
+++ b/tamingllms/_build/html/searchindex.js
@@ -1 +1 @@
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"debat": 9, "acknowledg": 9}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinxcontrib.bibtex": 9, "sphinx": 57}, "alltitles": {"About the Book": [[0, "about-the-book"]], "Contents": [[0, "contents"], [3, "contents"], [4, "contents"], [5, "contents"], [6, "contents"], [7, "contents"], [8, "contents"], [9, "contents"]], "Core Challenges We\u2019ll Address": [[0, "core-challenges-we-ll-address"]], "A Practical Approach": [[0, "a-practical-approach"]], "An Open Source Approach": [[0, "an-open-source-approach"]], "Open Source Book": [[0, "open-source-book"]], "A Note on Perspective": [[0, "a-note-on-perspective"]], "Who This Book Is For": [[0, "who-this-book-is-for"]], "Outcomes": [[0, "outcomes"]], "Prerequisites": [[0, "prerequisites"]], "Setting Up Your Environment": [[0, "setting-up-your-environment"]], "Code Repository": [[0, "code-repository"]], "Python Environment Setup": [[0, "python-environment-setup"]], "API Keys Configuration": [[0, "api-keys-configuration"]], "Troubleshooting Common Issues": [[0, "troubleshooting-common-issues"]], "About the Author": [[0, "about-the-author"]], "Preface": [[1, "preface"]], "Taming LLMs": [[2, "taming-llms"]], "A Practical Guide to LLM Pitfalls with Open Source Software": [[2, "a-practical-guide-to-llm-pitfalls-with-open-source-software"]], "Preference-Based Alignment": [[3, "preference-based-alignment"]], "Introduction": [[3, "introduction"], [5, "introduction"], [6, "introduction"], [7, "introduction"], [8, "introduction"], [9, "introduction"]], "From Raw Capabilities to Preference Alignment": [[3, "from-raw-capabilities-to-preference-alignment"]], "On the Misalignment of Language Models": [[3, "on-the-misalignment-of-language-models"]], "Aligning Language Models with Human Preferences": [[3, "aligning-language-models-with-human-preferences"]], "Supervised Fine-Tuning (SFT) for Model Alignment": [[3, "supervised-fine-tuning-sft-for-model-alignment"]], "Augmenting SFT with Human Preferences": [[3, "augmenting-sft-with-human-preferences"]], "Is Post-Training the Answer?": [[3, "is-post-training-the-answer"]], "Limitations": [[3, "limitations"]], "Model Collapse": [[3, "model-collapse"]], "Faking Alignment": [[3, "faking-alignment"]], "Case Study: Aligning a Language Model to a Policy": [[3, "case-study-aligning-a-language-model-to-a-policy"]], "Experimental Setup": [[3, "experimental-setup"]], "Deliverables": [[3, "deliverables"]], "A Note on smolLM2 Models": [[3, "a-note-on-smollm2-models"]], "Policy": [[3, "policy"]], "Preference Dataset - Synthetic Dataset Generation": [[3, "preference-dataset-synthetic-dataset-generation"]], "User Prompts": [[3, "user-prompts"]], "Rejected Responses": [[3, "rejected-responses"]], "Chosen Responses": [[3, "chosen-responses"]], "Generate DPO Dataset": [[3, "generate-dpo-dataset"]], "DPO-Based Optimization": [[3, "dpo-based-optimization"]], "Data Preparation": [[3, "data-preparation"]], "Fine-Tuning": [[3, "fine-tuning"]], "Vibe Check": [[3, "vibe-check"]], "Alignment Evaluation": [[3, "alignment-evaluation"]], "Discussion": [[3, "discussion"], [6, "discussion"], [6, "id41"], [9, "discussion"]], "Conclusion": [[3, "conclusion"], [4, "conclusion"], [5, "conclusion"], [6, "conclusion"], [7, "conclusion"], [8, "conclusion"], [9, "conclusion"]], "References": [[3, "references"], [4, "references"], [5, "references"], [6, "references"], [7, "references"], [8, "references"], [9, "references"]], "The Falling Cost Paradox": [[4, "the-falling-cost-paradox"]], "Why Optimization Matters More Than Ever": [[4, "why-optimization-matters-more-than-ever"]], "Right-Sizing LLMs: A Strategic Approach": [[4, "right-sizing-llms-a-strategic-approach"]], "Metrics": [[4, "metrics"], [5, "metrics"]], "Requirements": [[4, "requirements"]], "Business Requirements": [[4, "business-requirements"]], "Performance Requirements": [[4, "performance-requirements"]], "Operational Requirements": [[4, "operational-requirements"]], "Technical Requirements": [[4, "technical-requirements"]], "Quantization": [[4, "quantization"], [7, "quantization"]], "Check-list": [[4, "check-list"]], "The Evals Gap": [[5, "the-evals-gap"]], "Non-Deterministic Generative Machines": [[5, "non-deterministic-generative-machines"]], "Emerging Properties": [[5, "emerging-properties"]], "Problem Statement": [[5, "problem-statement"], [9, "problem-statement"]], "Evals of Traditional Software vs LLMs": [[5, "evals-table"]], "Evals Design": [[5, "evals-design"]], "LLM Application Testing Requirements Matrix": [[5, "validation-requirements"]], "Conceptual Overview": [[5, "conceptual-overview"]], "Design Considerations": [[5, "design-considerations"]], "Key Metrics for Evaluating Generative Tasks": [[5, "key-metrics"]], "Evaluators": [[5, "evaluators"]], "Model-Based Evaluation": [[5, "model-based-evaluation"]], "Evaluating Evaluators": [[5, "evaluating-evaluators"]], "Benchmarks and Leaderboards": [[5, "benchmarks-and-leaderboards"]], "Tools": [[5, "tools"], [9, "tools"]], "LightEval": [[5, "lighteval"]], "MMLU Econometrics Task Dataset sample": [[5, "mmlu-econometrics"]], "Model Families Evaluated Using LightEval": [[5, "model-families"]], "LangSmith": [[5, "langsmith"]], "PromptFoo": [[5, "promptfoo"]], "Comparison": [[5, "comparison"], [7, "comparison"], [7, "id37"]], "Comparison of Lighteval, LangSmith, and Promptfoo": [[5, "tool-comparison"]], "Managing Input Data": [[6, "managing-input-data"]], "Parsing Documents": [[6, "parsing-documents"]], "MarkItDown": [[6, "markitdown"]], "Docling": [[6, "docling"]], "Structured Data Extraction": [[6, "structured-data-extraction"]], "Retrieval-Augmented Generation": [[6, "retrieval-augmented-generation"]], "RAG Pipeline": [[6, "rag-pipeline"]], "Preparing the Knowledge Base": [[6, "preparing-the-knowledge-base"]], "Vector Database": [[6, "vector-database"]], "Reranking": [[6, "reranking"]], "LLMs with RAG": [[6, "llms-with-rag"]], "Challenges and Limitations": [[6, "challenges-and-limitations"]], "Will RAGs exist in the future?": [[6, "will-rags-exist-in-the-future"]], "A Note on Frameworks": [[6, "a-note-on-frameworks"]], "Case Studies": [[6, "case-studies"]], "Case Study I: Content Chunking with Contextual Linking": [[6, "case-study-i-content-chunking-with-contextual-linking"]], "Generating long-form content": [[6, "generating-long-form-content"]], "Case Study II: Quiz Generation with Citations": [[6, "case-study-ii-quiz-generation-with-citations"]], "Use Case": [[6, "use-case"]], "Implementation": [[6, "implementation"]], "Example Usage": [[6, "example-usage"]], "Local LLMs in Practice": [[7, "local-llms-in-practice"]], "Choosing your Model": [[7, "choosing-your-model"]], "Task Suitability": [[7, "task-suitability"]], "Benchmark results for Llama 2 family of models.": [[7, "llama2-benchmark"]], "Performance & Cost": [[7, "performance-cost"]], "Licensing": [[7, "licensing"]], "Open Source LLMs.": [[7, "open-source-llms"]], "Community Support": [[7, "community-support"]], "Customization": [[7, "customization"]], "Mistral fine-tuning costs as of December 22, 2024.": [[7, "mistral-costs"]], "Tools for Local LLM Deployment": [[7, "tools-for-local-llm-deployment"]], "Serving Models": [[7, "serving-models"]], "LLama.cpp": [[7, "llama-cpp"]], "Llamafile": [[7, "llamafile"]], "Ollama": [[7, "ollama"], [9, "ollama"]], "lama.cpp vs Ollama vs Llamafile Comparison": [[7, "feature-comparison-local"]], "UI": [[7, "ui"]], "LM Studio": [[7, "lm-studio"]], "Jan": [[7, "jan"]], "Open WebUI": [[7, "open-webui"]], "LM Studio vs Jan vs OpenWebUI Comparison": [[7, "feature-comparison-ui"]], "Case Study: The Effect of Quantization on LLM Performance": [[7, "case-study-the-effect-of-quantization-on-llm-performance"]], "Prompts Dataset": [[7, "prompts-dataset"]], "Quantization Levels": [[7, "quantization-levels"]], "Benchmarking": [[7, "benchmarking"], [8, "benchmarking"]], "Results": [[7, "results"]], "Quantization Benchmarks": [[7, "quantization-benchmarks"]], "Benchmarking Hardware": [[7, "benchmarking-hardware"]], "Takeaways": [[7, "takeaways"], [8, "takeaways"]], "Safety": [[8, "safety"]], "Safety Risks": [[8, "safety-risks"]], "General AI Safety Risks": [[8, "general-ai-safety-risks"]], "Amplified Existing Harms and Novel Risks": [[8, "amplified-existing-harms-and-novel-risks"]], "Risks Associated with Autonomous AI": [[8, "risks-associated-with-autonomous-ai"]], "Exacerbating Factors": [[8, "exacerbating-factors"]], "LLMs Specific Safety Risks": [[8, "llms-specific-safety-risks"]], "Guidance": [[8, "guidance"]], "Governments & Organizations": [[8, "governments-organizations"]], "Private Sector": [[8, "private-sector"]], "OpenAI": [[8, "openai"]], "Anthropic": [[8, "anthropic"]], "Google": [[8, "google"]], "Rubrics": [[8, "rubrics"]], "MLCommons AI Safety Benchmark": [[8, "mlcommons-ai-safety-benchmark"]], "Centre for the Governance of AI Rubric": [[8, "centre-for-the-governance-of-ai-rubric"]], "Pourquoi": [[8, "pourquoi"]], "Approaches": [[8, "approaches"]], "Red Teaming": [[8, "red-teaming"]], "Constitutional AI": [[8, "constitutional-ai"]], "Explainable AI (XAI)": [[8, "explainable-ai-xai"]], "Designing a Safety Plan": [[8, "designing-a-safety-plan"]], "Phase 1. Policy Definition": [[8, "phase-1-policy-definition"]], "Phase 2. User Research & Risk Identification": [[8, "phase-2-user-research-risk-identification"]], "Phase 3. Evaluation Framework": [[8, "phase-3-evaluation-framework"]], "Phase 4. Safety Architecture Design": [[8, "phase-4-safety-architecture-design"]], "Phase 5. Implementation & Tools Selection": [[8, "phase-5-implementation-tools-selection"]], "Phase 6. Go-to-Market": [[8, "phase-6-go-to-market"]], "Common Pitfalls": [[8, "common-pitfalls"]], "Technical Implementation Components": [[8, "technical-implementation-components"]], "Benchmarks & Datasets": [[8, "benchmarks-datasets"]], "SALAD-Bench": [[8, "salad-bench"]], "TruthfulQA": [[8, "truthfulqa"]], "HarmBench": [[8, "harmbench"]], "SafeBench": [[8, "safebench"]], "Tools & Techniques": [[8, "tools-techniques"]], "Representative Safety Layer Risk Map.": [[8, "safety-layer-table"]], "Rules-Based Safety Filtering": [[8, "rules-based-safety-filtering"]], "Rules-Based Safety Filtering Tools.": [[8, "safety-layer-tools"]], "LLM-Based Safety Filtering": [[8, "llm-based-safety-filtering"]], "Custom Moderation": [[8, "custom-moderation"]], "Case Study: Implementing a Safety Filter": [[8, "case-study-implementing-a-safety-filter"]], "Evals Dataset": [[8, "evals-dataset"]], "Bad Samples": [[8, "bad-samples"]], "Good Samples": [[8, "good-samples"]], "Safety Filters": [[8, "safety-filters"]], "LLM-Guard": [[8, "llm-guard"]], "Mistral Moderation API": [[8, "mistral-moderation-api"]], "OpenAI Moderation API": [[8, "openai-moderation-api"]], "Custom Judge Validator": [[8, "custom-judge-validator"]], "Structured Output": [[9, "structured-output"]], "Techniques": [[9, "techniques"]], "Prompt Engineering": [[9, "prompt-engineering"]], "JSON Mode (Fine-Tuned)": [[9, "json-mode-fine-tuned"]], "Logit Post-Processing": [[9, "logit-post-processing"]], "Outlines": [[9, "outlines"]], "LangChain": [[9, "langchain"]], "Best Practices": [[9, "best-practices"]], "Comparing Solutions": [[9, "comparing-solutions"]], "Structured Output Frameworks Comparison": [[9, "structured-output-frameworks"]], "Research and Ongoing Debate": [[9, "research-and-ongoing-debate"]], "Acknowledgements": [[9, "acknowledgements"]]}, "indexentries": {}})
\ No newline at end of file
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"constitutional-ai"]], "Explainable AI (XAI)": [[8, "explainable-ai-xai"]], "Designing a Safety Plan": [[8, "designing-a-safety-plan"]], "Phase 1. Policy Definition": [[8, "phase-1-policy-definition"]], "Phase 2. User Research & Risk Identification": [[8, "phase-2-user-research-risk-identification"]], "Phase 3. Evaluation Framework": [[8, "phase-3-evaluation-framework"]], "Phase 4. Safety Architecture Design": [[8, "phase-4-safety-architecture-design"]], "Phase 5. Implementation & Tools Selection": [[8, "phase-5-implementation-tools-selection"]], "Phase 6. Go-to-Market": [[8, "phase-6-go-to-market"]], "Common Pitfalls": [[8, "common-pitfalls"]], "Technical Implementation Components": [[8, "technical-implementation-components"]], "Benchmarks & Datasets": [[8, "benchmarks-datasets"]], "SALAD-Bench": [[8, "salad-bench"]], "TruthfulQA": [[8, "truthfulqa"]], "HarmBench": [[8, "harmbench"]], "SafeBench": [[8, "safebench"]], "Tools & Techniques": [[8, "tools-techniques"]], "Representative Safety Layer Risk Map.": [[8, "safety-layer-table"]], "Rules-Based Safety Filtering": [[8, "rules-based-safety-filtering"]], "Rules-Based Safety Filtering Tools.": [[8, "safety-layer-tools"]], "LLM-Based Safety Filtering": [[8, "llm-based-safety-filtering"]], "Custom Moderation": [[8, "custom-moderation"]], "Case Study: Implementing a Safety Filter": [[8, "case-study-implementing-a-safety-filter"]], "Evals Dataset": [[8, "evals-dataset"]], "Bad Samples": [[8, "bad-samples"]], "Good Samples": [[8, "good-samples"]], "Safety Filters": [[8, "safety-filters"]], "LLM-Guard": [[8, "llm-guard"]], "Mistral Moderation API": [[8, "mistral-moderation-api"]], "OpenAI Moderation API": [[8, "openai-moderation-api"]], "Custom Judge Validator": [[8, "custom-judge-validator"]], "Structured Output": [[9, "structured-output"]], "Techniques": [[9, "techniques"]], "Prompt Engineering": [[9, "prompt-engineering"]], "JSON Mode (Fine-Tuned)": [[9, "json-mode-fine-tuned"]], "Logit Post-Processing": [[9, "logit-post-processing"]], "Outlines": [[9, "outlines"]], "LangChain": [[9, "langchain"]], "Best Practices": [[9, "best-practices"]], "Comparing Solutions": [[9, "comparing-solutions"]], "Structured Output Frameworks Comparison": [[9, "structured-output-frameworks"]], "Research and Ongoing Debate": [[9, "research-and-ongoing-debate"]], "Acknowledgements": [[9, "acknowledgements"]]}, "indexentries": {}})
\ No newline at end of file
diff --git a/tamingllms/_build/jupyter_execute/markdown/intro.ipynb b/tamingllms/_build/jupyter_execute/markdown/intro.ipynb
index b7626d2..4746ef4 100644
--- a/tamingllms/_build/jupyter_execute/markdown/intro.ipynb
+++ b/tamingllms/_build/jupyter_execute/markdown/intro.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "789163a3",
+   "id": "fa7b8918",
    "metadata": {},
    "source": [
     "(intro)=\n",
@@ -167,13 +167,7 @@
     "- For package conflicts, try creating a fresh virtual environment or use a package manager like `poetry`\n",
     "- Check the book's repository issues page for known problems and solutions\n",
     "\n",
-    "Now that your environment is set up, let's begin our exploration of LLM challenges.\n",
-    "\n",
-    "## About the Author\n",
-    "\n",
-    "Tharsis Souza (Ph.D. Computer Science, UCL University of London) is a computer scientist and product leader specializing in AI-based products. He is a Lecturer at Columbia University's Master of Science program in Applied Analytics, (*incoming*) Head of Product, Equities at Citadel, and former Senior VP at Two Sigma Investments. He mentors under-represented students & working professionals to help create a more diverse global AI ecosystem.\n",
-    "\n",
-    "With over 15 years of experience delivering technology products across startups and Fortune 500 companies, he is also an author of numerous scholarly publications and a frequent speaker at academic and business conferences. Grounded on academic background and drawing from practical experience building and scaling up products powered by language models at early-stage startups, major institutions as well as contributing to open source projects, he brings a unique perspective on bridging the gap between LLMs promised potential and their practical implementation challenges to enable the next generation of AI-powered products."
+    "Now that your environment is set up, let's begin our exploration of LLM challenges."
    ]
   }
  ],
diff --git a/tamingllms/markdown/intro.md b/tamingllms/markdown/intro.md
index ab10fe5..19f8b41 100644
--- a/tamingllms/markdown/intro.md
+++ b/tamingllms/markdown/intro.md
@@ -172,10 +172,3 @@ Never share your `.env` file or commit it to version control. It contains sensit
 - Check the book's repository issues page for known problems and solutions
 
 Now that your environment is set up, let's begin our exploration of LLM challenges.
-
-## About the Author
-
-Tharsis Souza (Ph.D. Computer Science, UCL University of London) is a computer scientist and product leader specializing in AI-based products. He is a Lecturer at Columbia University's Master of Science program in Applied Analytics, (*incoming*) Head of Product, Equities at Citadel, and former Senior VP at Two Sigma Investments. He mentors under-represented students & working professionals to help create a more diverse global AI ecosystem.
-
-With over 15 years of experience delivering technology products across startups and Fortune 500 companies, he is also an author of numerous scholarly publications and a frequent speaker at academic and business conferences. Grounded on academic background and drawing from practical experience building and scaling up products powered by language models at early-stage startups, major institutions as well as contributing to open source projects, he brings a unique perspective on bridging the gap between LLMs promised potential and their practical implementation challenges to enable the next generation of AI-powered products.
-