AI Chat & Conversations

AI Chat & Conversations

DeepTalk’s AI chat feature enables interactive conversations with your transcribed content, allowing you to explore, analyze, and extract insights through natural language conversations. This guide covers all aspects of the chat system.

Chat Overview

What is AI Chat in DeepTalk?

AI Chat allows you to have conversations with your transcripts as if you were discussing the content with a knowledgeable assistant. The AI can:

  • Answer questions about transcript content
  • Provide summaries of specific topics or sections
  • Find relevant information across your transcript library
  • Explain complex discussions in simpler terms
  • Connect related concepts from different parts of conversations
  • Generate insights based on transcript analysis

Chat Capabilities

Interactive Exploration:

  • Ask questions about specific topics discussed
  • Request clarification of unclear or complex points
  • Explore relationships between different concepts
  • Deep-dive into particular aspects of conversations

Content Analysis:

  • Generate summaries of any length or focus
  • Identify key themes and patterns
  • Extract action items and decisions
  • Analyze speaker perspectives and viewpoints

Information Retrieval:

  • Find specific quotes or statements
  • Locate discussions about particular topics
  • Search for mentions of people, projects, or concepts
  • Cross-reference information across multiple transcripts

Conversation Modes

Vector Search Only Mode

Best for: Quick fact-finding and exact quote discovery

How it works:

  • Searches your transcripts for relevant content
  • Returns exact excerpts with timestamps and relevance scores
  • No AI interpretation or analysis
  • Fastest response time

When to use:

  • Finding specific quotes or statements
  • Locating exact information quickly
  • When you want uninterpreted source material
  • Fact-checking and verification

Example interaction:

You: "What did John say about the budget?"
Response: [Relevant excerpt with timestamp]
"John: I think we need to increase the marketing budget by 15% next quarter."
[Transcript: Q3_Planning_Meeting.mp3, 14:32, Relevance: 0.89]

Best for: General conversation and analysis

How it works:

  • Retrieves relevant transcript content
  • Sends context to AI for interpretation and analysis
  • Provides natural, conversational responses
  • Balances speed with comprehensive answers

When to use:

  • General questions about transcript content
  • Requesting summaries and explanations
  • Exploring themes and patterns
  • Most day-to-day chat interactions

Example interaction:

You: "What were the main concerns about the new product launch?"
AI: "Based on the meeting transcript, there were three main concerns:
1. Timeline pressure - several team members worried about the aggressive launch date
2. Resource allocation - questions about whether the marketing team has sufficient budget
3. Quality assurance - concerns about adequate testing time before release
The discussion centered around balancing speed to market with product quality."

Direct LLM Mode

Best for: Comprehensive analysis and complex queries

How it works:

  • Sends entire transcript (or large portions) directly to AI
  • Provides most comprehensive context
  • Enables deep analysis and complex reasoning
  • Slower but most thorough responses

When to use:

  • Complex analysis requiring full context
  • Strategic insights and recommendations
  • Comprehensive summaries of entire conversations
  • Deep dive analysis of themes and patterns

Example interaction:

You: "Analyze the decision-making process and recommend improvements"
AI: "After reviewing the entire transcript, I notice several patterns in your team's decision-making process:

[Detailed analysis with specific examples and recommendations follows...]"

Chat Interface and Navigation

Starting Conversations

From Transcript View:

  • Open any transcript in detail view
  • Click “Chat” button or tab
  • Begin conversation immediately with that transcript’s context

From Project View:

  • Open project containing multiple transcripts
  • Access project-level chat for cross-transcript conversations
  • AI has access to all transcripts in the project

From Global Chat:

  • Access chat from main navigation
  • Search and select transcripts to include in conversation
  • Create custom conversation contexts

Conversation Management

Chat History:

  • All conversations are automatically saved
  • Access previous conversations from chat history
  • Search through conversation history by topic or date
  • Bookmark important conversations for quick access

Context Management:

  • Single transcript: Chat focuses on one specific transcript
  • Multiple transcripts: Include multiple transcripts in conversation context
  • Project-wide: Chat with all transcripts in a project
  • Custom selection: Choose specific transcripts for targeted conversations

Session Management:

  • Continuous sessions: Ongoing conversations with memory
  • Fresh starts: Begin new conversations without previous context
  • Context switching: Change transcript focus during conversation
  • Memory limits: Automatic conversation summarization for long chats

Conversation Techniques

Effective Question Formulation

Specific Questions:

  • “What did Sarah say about the timeline in minute 15-20?”
  • “Find all mentions of ‘budget constraints’ in this transcript”
  • “What were the three main action items decided?”

Analytical Questions:

  • “What are the underlying concerns about this project?”
  • “How do different team members view this proposal?”
  • “What patterns do you see in the decision-making process?”

Comparative Questions:

  • “How does this meeting compare to last week’s discussion?”
  • “What has changed in the team’s perspective over these conversations?”
  • “Compare the concerns raised by technical vs. business stakeholders”

Advanced Conversation Strategies

Progressive Exploration:

  1. Start with broad questions for overview
  2. Drill down into specific areas of interest
  3. Ask for clarification or additional detail
  4. Request connections to other relevant content

Multi-Turn Analysis:

  1. Ask for initial analysis
  2. Request specific evidence or examples
  3. Explore implications and consequences
  4. Generate recommendations or next steps

Cross-Reference Investigation:

  1. Start with one transcript or topic
  2. Ask AI to find related content in other transcripts
  3. Explore connections and patterns across multiple conversations
  4. Synthesize insights from multiple sources

Memory and Context Management

Conversation Memory

Short-Term Memory:

  • Remembers current conversation context
  • Maintains topic continuity within sessions
  • Recalls previous questions and answers in current chat
  • Preserves conversation flow and references

Long-Term Memory:

  • Saves important conversation highlights
  • Remembers user preferences and interests
  • Tracks frequently asked questions and topics
  • Maintains context across multiple chat sessions

Memory Optimization:

  • Automatic summarization of long conversations
  • Intelligent context pruning to maintain relevance
  • User-controlled memory management and cleanup
  • Conversation bookmarking for important discussions

Context Configuration

Context Size Management:

  • Number of chunks: How many relevant sections to include (1-10)
  • Chunk size: Size of each context section
  • Overlap handling: Prevent information gaps between chunks
  • Relevance threshold: Minimum similarity score for inclusion

Dynamic Context:

  • Adaptive sizing: Adjust context based on question complexity
  • Smart filtering: Include most relevant information for each query
  • Context switching: Change focus during conversation as needed
  • Multi-source integration: Combine information from multiple transcripts

Advanced Chat Features

Semantic Understanding

Concept Recognition:

  • Understands synonyms and related terms
  • Recognizes concepts even when expressed differently
  • Connects ideas across different conversation segments
  • Identifies implicit meanings and context

Relationship Mapping:

  • Maps relationships between speakers, topics, and concepts
  • Understands conversation flow and dependencies
  • Identifies cause-and-effect relationships
  • Tracks decision chains and reasoning processes

Specialized Queries

Timeline Analysis:

  • “Walk me through the chronology of this decision”
  • “How did the team’s opinion evolve during this meeting?”
  • “What happened between the initial proposal and final decision?”

Speaker-Focused Analysis:

  • “What are John’s main concerns throughout these meetings?”
  • “How do technical and business perspectives differ on this issue?”
  • “Who are the decision-makers vs. influencers in these discussions?”

Topic Deep-Dives:

  • “Tell me everything discussed about the budget”
  • “Analyze all references to customer feedback”
  • “What are all the risks mentioned across these conversations?”

Integration with Other Features

Search Integration:

  • Use chat results to inform search queries
  • Reference search results in conversations
  • Combine keyword and semantic search approaches
  • Export conversation insights to enhance searchability

Analysis Integration:

  • Reference automated analysis results in conversations
  • Use chat to explore and validate AI-generated insights
  • Combine conversational exploration with structured analysis
  • Generate custom analysis through targeted questioning

Export Integration:

  • Export conversation transcripts and insights
  • Include chat-generated summaries in reports
  • Create documentation from conversation highlights
  • Share conversation insights with team members

Chat Optimization and Best Practices

Performance Optimization

Response Time Optimization:

  • Choose appropriate conversation mode for your needs
  • Optimize context size for balance of speed and completeness
  • Use specific questions for faster, focused responses
  • Cache frequently accessed content for improved performance

Quality Optimization:

  • Provide clear, specific questions for better responses
  • Use follow-up questions to refine and clarify answers
  • Reference specific parts of transcripts when possible
  • Give feedback to improve AI understanding of your needs

Conversation Workflow

Systematic Exploration:

  1. Overview: Start with broad questions about main topics
  2. Deep dive: Focus on areas of particular interest
  3. Cross-reference: Explore connections to other content
  4. Synthesis: Generate conclusions and recommendations
  5. Documentation: Save important insights and decisions

Collaborative Usage:

  • Share conversation links with team members
  • Use chat insights to inform team discussions
  • Create conversation templates for common analysis needs
  • Develop team protocols for effective chat usage

Privacy and Security

Data Handling:

  • All conversations processed locally when using local AI services
  • No conversation data sent to external services unless explicitly configured
  • User control over conversation history retention
  • Secure handling of sensitive conversation content

Access Control:

  • Conversation access based on transcript permissions
  • Team sharing controls for collaborative conversations
  • Audit trails for conversation access and sharing
  • Integration with organizational security policies

Troubleshooting Chat Issues

Common Problems

Poor Response Quality:

  • Vague questions: Ask more specific, focused questions
  • Insufficient context: Include more relevant transcript content
  • Wrong mode: Choose appropriate conversation mode for your needs
  • Context overload: Reduce context size if responses become generic

Performance Issues:

  • Slow responses: Reduce context size or switch to faster mode
  • Memory problems: Clear conversation history or restart chat
  • Service issues: Check AI service connection and configuration
  • Resource constraints: Monitor system resources during chat usage

Context Problems:

  • Missing information: Verify relevant transcripts are included
  • Outdated context: Refresh context with latest transcript versions
  • Conflicting information: Clarify which transcripts should be prioritized
  • Scope confusion: Clearly define conversation boundaries and focus

Optimization Solutions

Response Quality:

  • Use progressive questioning techniques
  • Provide specific examples in questions
  • Ask for citations and references in responses
  • Use follow-up questions to clarify and expand

Performance Enhancement:

  • Optimize AI service configuration
  • Use appropriate hardware resources
  • Monitor and manage conversation memory
  • Implement caching strategies for frequently accessed content

User Experience:

  • Develop question templates for common use cases
  • Create conversation workflows for systematic analysis
  • Train team members on effective chat techniques
  • Establish best practices for different types of analysis

Next: Explore search and discovery features →