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We’ll begin this webinar by tracing the evolution of search and how it led to the powerful capabilities we rely on in Elasticsearch today. From analysis to relevance scoring, we’ll break down how search actually works under the hood. Our focus will be on the core principles of lexical search, why it remains essential even in the age of AI, and the practical techniques for building high-quality search experiences. You’ll learn how queries are analyzed, matched, scored, and tuned to deliver strong precision, balanced recall, and an exceptional user experience.
You’ll learn how data moves through the stack, how to transform it for better insights, and how to visualize it using Kibana dashboards and alerts.Elastic Observability powers visibility across logs, metrics, and traces — but understanding how it all connects takes practice.
- Explain what makes search effective and why lexical search still matters
- Understand how queries flow through Elasticsearch’s analysis and scoring pipeline
- Configure and experiment with analyzers, token filters, and indexing strategies
- Apply relevance tuning techniques to improve precision and recall
- Build strong search experiences using heuristics, boosts, and query design
- Recognize when to extend beyond lexical search into semantic or hybrid approaches
- Evolution of search
- Lexical Search in a Post-AI World
- What makes search “good”
- Query lifecycle in Elasticsearch
- Index-time vs query-time analysis
- Tokenization, normalization, and filtering
- Character filters, tokenizers, token filters
- Common analyzers: standard, whitespace, ngram, shingle, synonym
- Demo: how different analyzers transform the same input
- Inverted index, term frequency, field normalization
- Scoring essentials: BM25, TF-IDF
- Boosting, field weights, and query structures
- Custom analyzers & domain tuning
- Synonyms, shingles, stopwords, and lemmatization
- Designing for the recall–relevance balance
- Heuristic ranking: recency, popularity, metadata boosts
- Function score queries, fuzzy matching, suggesters
- Demo: relevance tuning workflow
- Search as engineering + UX
- When to consider semantic/hybrid search
- Key takeaways & tuning checklist
Speakers
Harsh brings nearly five years of hands-on experience with Elasticsearch, Logstash, Kibana, Python, and modern DevOps practices. As an ELK specialist, he builds high-performance data pipelines, designs observability dashboards, and strengthens infrastructure reliability at scale.
As an Elastic Certified Observability Engineer, Harsh combines real-world expertise with a passion for turning logs and metrics into actionable insights that help teams optimize performance, reduce downtime, and scale confidently.