IVQA 851-900

851. Short-term vs. Long-term Memory in LLM Agents

  • Short-term: In-context memory (token window) for transient facts or dialogue within a session.

  • Long-term: Stored external memory (e.g., vector DB or database) that persists across sessions and can be retrieved via embeddings.

852. Building Episodic Memory for Multi-session Assistants

  • Segment conversations into discrete episodes (sessions), embed them, and store with metadata (user ID, timestamp, tags). Retrieve relevant episodes using semantic search.

853. Vector-based Memory Injection

  • Retrieved vectors (context) are appended to the prompt. This improves relevance but must be filtered for quality. Poor injection can confuse the model.

854. Avoiding Memory Pollution

  • Filter out redundant, low-signal, or contradictory interactions. Use scoring functions for relevance and diversity. Prune based on decay functions or feedback loops.

855. Personalizing Memory on Shared Infrastructure

  • Namespace memory by user ID or tenant. Use contextual filters to scope memory retrieval. Optionally encrypt user-specific segments.

856. Memory Expiration/Pruning

  • TTL (time-to-live), recency-based pruning, or score decay. Garbage collection strategies can be triggered periodically or when memory exceeds limits.

857. Context Window Budgeting

  • Prioritize recent and high-relevance context. Chunk data effectively and summarize if over budget. Use ranking algorithms to select top-k entries for injection.

858. Task-specific Evolving Memory

  • Use task IDs and role-based metadata to update evolving memory (e.g., project state, code version). Track progress and overwrite only when confidence is high.

859. Unified Architecture for User, Session, and Tool Memory

  • Layered memory system:

    • User Profile Memory

    • Session (episodic) Memory

    • Tool Usage Memory (e.g., tool-call logs)

    • All retrievable via embedding+metadata filters.

860. Evaluating Memory Fidelity and Recency Bias

  • Use retrieval audits: check if most relevant past data is returned. For recency bias, balance old vs. new data in training retrieval ranking models.

861. Making LLMs Time-Aware

  • Inject current date/time as a system prompt variable. Use retrieval to include historical or future-scheduled facts. Some models (e.g., Claude, GPT-4) benefit from explicit timeline formatting.

862. Techniques for Sequence/Timeline Reasoning

  • Use temporal embeddings or event graphs. Reinforce with prompt chaining or structured reasoning templates (e.g., “Step 1: sort by date”).

863. Representing Temporal Facts in Embeddings

  • Use sentence encoders enriched with temporal signals (e.g., BERT + timestamp). Tag facts with [DATE] tokens during indexing to improve retrieval fidelity.

864. Prompting for Future-State Dependencies

  • Use conditionals and explicit temporal markers: “If today is Monday and the event is in 3 days, when is the event?” Include intermediate reasoning prompts.

865. Common Calendar Math Errors and Fixes

  • Off-by-one, month-end issues, daylight savings confusion. Fix via calendar APIs, not model-only. Let LLM reason and verify with tooling (e.g., dateutil, Chronyk).

866. Managing Context for Recurring Agents

  • Use persistent state objects scoped by recurrence cycle (e.g., weekly planning agent). Include memory snapshots with tagged recurrence patterns.

867. Asking for Clarification in Date Ranges

  • Prompt tuning: “Please clarify the date range. Do you mean calendar week or rolling 7 days?” Use masked confirmations in ambiguous scenarios.

868. Benchmarking Event Chain Understanding

  • Use tests like MMLU Temporal or custom chains like:

    • “Event A happened before B and after C. Order them.” Evaluate accuracy, latency, and consistency across trials.

869. Simulating Time Progression

  • Maintain simulated time index in memory. Update “now” context dynamically. Useful for training agents to plan, review, and learn over time.

870. Time-aware Prompts + External Calendars

  • Integrate with Google/Outlook APIs. Use event triggers or CRON-like scheduling logic. Inject calendar events as part of prompt context.

871. Communication Between Agents

  • Message-passing protocol (e.g., JSON + metadata). Use shared context memory or message bus (e.g., Redis pub-sub or LangGraph channels).

872. Agent Role Taxonomy

  • Define agents as:

    • Executor

    • Critic

    • Planner

    • Retriever

    • Monitor Improves modularity and chain-of-responsibility.

873. Coordination in Content Workflow

  • Drafting Agent → Reviewing Agent → Formatting Agent pipeline. Use output metadata tags (e.g., {status: draft}) to indicate phase transitions.

874. Agents Critiquing Each Other

  • Use system prompt instructions to review peer output and generate structured feedback. Reward useful criticism using a reinforcement signal.

875. Arbitration When Agents Disagree

  • Use:

    • Voting

    • LLM-as-Judge

    • Confidence scoring

    • Escalation to human review.

876. Avoiding Knowledge Redundancy

  • Assign agents domain-specific or task-specific memory scopes. Use memory deduplication and role-relevant filtering.

877. Shared Memory in Teams

  • Centralized vector DB with role-based tagging and access filtering. Can be segmented by task or phase.

878. Agent Handoff & Task Completion

  • Include handoff_summary, context_payload, and next_agent metadata. Use checksums to verify task completeness.

879. Monitoring Multi-Agent Decisions

  • Use structured logs with timestamps, memory used, agent outputs. Store in audit-compliant DB with retrieval interface.

880. Human-in-the-Loop for Agent Teams

  • Assign human as final reviewer. Let them see snapshots and memory states. Provide editable feedback hooks.

881. End-to-End GenAI Content Workflow

  • Draft → Critique → Rewrite → Finalize. Chain agents with quality gates (score thresholds or feedback filters).

882. Validating Tone, Length, Brand Guidelines

  • Use LLM-based checkers with prompt templates. Validate against checklists or regex/embedding-based constraints.

883. Tools for Testing LLM Output

  • Examples:

    • Grammarly API

    • LanguageTool

    • Gingersoftware

    • Custom prompt validators with test cases.

884. Structured Metadata in Output

  • Embed metadata like topic, sentiment, readability using tags (YAML/JSON frontmatter). Useful for downstream pipelines.

885. Factual Consistency in Long-form Generation

  • Use RAG-based checkpoints and content verification agents. Segment large docs and inject relevant context.

886. Incorporating Human Feedback

  • Collect labeled data (e.g., “like/dislike”, error reason). Use preference tuning or reinforcement learning from human feedback (RLHF).

887. Newsletter Pipeline from Logs

  • Parse structured logs → Summarize activity → Rewrite headlines → Format with template → Push to email tool.

888. Evaluating Content Diversity vs. Repetition

  • N-gram repetition analysis, embedding similarity, diversity scores. Introduce penalties for repetition in decoding (e.g., no_repeat_ngram_size).

889. Retry/Fallback for Low Confidence Outputs

  • Use confidence scoring (entropy, embedding distance). Retry with rephrased prompt, different model, or ask for clarification.

890. Integrating SEO in Prompts

  • Inject keywords, headline formulas (AIDA, PAS), meta-description tags. Use embedding-based keyword matching to validate.

891. Image + Text Reasoning for Layouts

  • Use OCR + layout parsers (e.g., LayoutLM). Tokenize visual elements with spatial relationships before reasoning.

892. CLIP / BLIP for Visual Grounding

  • CLIP: Image-text similarity. BLIP: Caption generation + Q&A. Used to anchor text prompts with visual grounding for better multimodal alignment.

893. Table + Paragraph Reasoning

  • Detect table structure (via table parsers or pandas). Use hybrid prompts that reference both table rows and narrative sections.

894. OCR → Captioning → Q&A Architecture

  • Step-by-step pipeline:

    • Tesseract/DocTR → BLIP (caption) → LLM Q&A agent. Maintain consistent IDs across stages for context mapping.

895. Chunking Multi-modal PDFs

  • Split by layout structure (e.g., page, section, image box). Store chunks with modality tags and embed using modality-specific models.

896. Evaluating Multi-modal Reasoning

  • Use benchmarks like MMMU, ScienceQA. Evaluate consistency across modalities and hallucination rates.

897. Fine-tuning for Domain Visual Tasks

  • Use image-text pairs (e.g., X-ray and diagnosis for radiology). Fine-tune on task-specific data using contrastive loss or instruction tuning.

898. Processing Video Transcripts with Speaker Turns

  • ASR with diarization → Segment by speaker → Summarize/summarize by turn → Thread-level memory for agents.

899. Referring Back to Visual/Spatial Memory

  • Use visual memory cache (e.g., previous screen layouts). Reference by spatial token: “top-left diagram” or “first chart”.

900. Safe UX Patterns for Multi-modal Output

  • Use tabs (text/image), expandable sections, alt text. Ensure clarity, especially when presenting visual results with LLM commentary.

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