diff --git a/python/functions/datascience/automatic_eda/chapters/analisis_llm.py b/python/functions/datascience/automatic_eda/chapters/analisis_llm.py
new file mode 100644
index 00000000..e182e6a0
--- /dev/null
+++ b/python/functions/datascience/automatic_eda/chapters/analisis_llm.py
@@ -0,0 +1,221 @@
+"""LLM analysis chapter (ANÁLISIS LLM) — the interpretive layer, next to overview.
+
+Third reference chapter for AutomaticEDA. Renders the ``llm`` block that the
+``eda`` group function ``eda_llm_insights`` already produced and stored in the
+``TableProfile`` — it does NOT call the LLM nor recompute anything. The block is
+turned into clean, markdown-style document blocks so it reads as a real chapter
+(table summary, row meaning, data dictionary, suggested analyses, cleaning
+suggestions, PII findings) and, crucially, **nothing is ever cut** in PDF or
+PPTX:
+
+* Prose (summary, row meaning) → ``Markdown`` blocks the renderers wrap to whole
+  lines, so no word is lost no matter how long the text is.
+* The data dictionary and PII findings → ``DataTable`` blocks the paginator
+  splits by rows (repeating the header) and whose long cells wrap inside their
+  column — wide, multi-row tables never overflow a page/slide.
+* Cleaning suggestions and suggested analyses → ``Markdown`` bullet lists; each
+  item is a whole line the renderer wraps, never truncated mid-entry.
+
+Position: this chapter is declared in ``chapters_registry.CHAPTER_ORDER`` right
+after ``overview`` so the interpretation sits next to the table preview, as the
+user asked ("va junto al overview").
+
+Data source: the ``llm`` dict produced by ``eda_llm_insights`` (group ``eda``),
+read from ``profile['llm']`` (or ``ctx['llm']`` as a fallback). Shape::
+
+    {
+      "summary": str,            # what the table is, 2-3 sentences
+      "row_meaning": str,        # what one row represents / granularity
+      "dictionary": [ {"column","description","business_meaning","unit"} ],
+      "pii": [ {"column","kind","severity"} ],
+      "cleaning": [str],         # cleaning / transformation suggestions
+      "analyses": [str],         # suggested questions / analyses / hypotheses
+    }
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+Reads everything defensively (``.get``) and NEVER raises; returns ``None`` when
+the profile carries no LLM block (e.g. ``profile_table`` ran without
+``run_llm``), so the chapter is simply omitted from the document.
+"""
+
+from __future__ import annotations
+
+from .. import model
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "analisis_llm"
+CHAPTER_TITLE = "Análisis LLM"
+
+# Key under which eda_llm_insights stores its interpretive block in the profile.
+LLM_KEY = "llm"
+
+
+def _clean_text(value) -> str:
+    """Coerce a value to a single trimmed line (collapse inner newlines).
+
+    Used for bullet items so each suggestion stays a single markdown bullet the
+    renderer wraps; never drops content, only normalizes whitespace.
+    """
+    text = model._safe_str(value).strip()
+    if not text:
+        return ""
+    return " ".join(text.split())
+
+
+def _para(value) -> str:
+    """Coerce a value to trimmed prose, preserving paragraph breaks."""
+    text = model._safe_str(value).strip()
+    if not text:
+        return ""
+    # Keep blank-line paragraph breaks; collapse runs of spaces/tabs per line.
+    lines = [" ".join(ln.split()) for ln in text.splitlines()]
+    out: list = []
+    for ln in lines:
+        if ln or (out and out[-1] != ""):
+            out.append(ln)
+    return "\n".join(out).strip()
+
+
+def _bullets(items) -> str:
+    """Build a markdown bullet list from a sequence of strings.
+
+    Each item becomes one ``- ...`` line (a whole, wrappable unit). Empty items
+    and non-list inputs are handled gracefully; returns "" when there is nothing.
+    """
+    if isinstance(items, str):
+        items = [items]
+    if not isinstance(items, (list, tuple)):
+        return ""
+    lines = []
+    for it in items:
+        text = _clean_text(it)
+        if text:
+            lines.append(f"- {text}")
+    return "\n".join(lines)
+
+
+def _summary_blocks(llm: dict) -> list:
+    """Heading + prose for the table summary, or [] if absent."""
+    text = _para(llm.get("summary"))
+    if not text:
+        return []
+    return [model.Heading(text="Resumen de la tabla", level=2),
+            model.Markdown(text=text)]
+
+
+def _row_meaning_blocks(llm: dict) -> list:
+    """Heading + prose for what one row represents, or [] if absent."""
+    text = _para(llm.get("row_meaning"))
+    if not text:
+        return []
+    return [model.Heading(text="Significado de una fila", level=2),
+            model.Markdown(text=text)]
+
+
+def _dictionary_block(llm: dict):
+    """DataTable for the data dictionary, or None if absent/empty.
+
+    Columns: Columna / Descripción / Significado de negocio / Unidad. The
+    paginator splits this by rows repeating the header and wraps long cells, so a
+    long dictionary (many columns) never gets cut.
+    """
+    entries = llm.get("dictionary")
+    if not isinstance(entries, (list, tuple)) or not entries:
+        return None
+    header = ["Columna", "Descripción", "Significado de negocio", "Unidad"]
+    rows = []
+    for e in entries:
+        if not isinstance(e, dict):
+            # Be tolerant: a bare string still shows up as a description row.
+            rows.append(["—", _clean_text(e), "", ""])
+            continue
+        rows.append([
+            _clean_text(e.get("column")) or "—",
+            _clean_text(e.get("description")),
+            _clean_text(e.get("business_meaning")),
+            _clean_text(e.get("unit")),
+        ])
+    if not rows:
+        return None
+    return model.DataTable(header=header, rows=rows, title="Diccionario de datos")
+
+
+def _analyses_blocks(llm: dict) -> list:
+    """Heading + bullet list of suggested analyses, or [] if absent."""
+    bullets = _bullets(llm.get("analyses"))
+    if not bullets:
+        return []
+    return [model.Heading(text="Análisis sugeridos", level=2),
+            model.Markdown(text=bullets)]
+
+
+def _cleaning_blocks(llm: dict) -> list:
+    """Heading + bullet list of cleaning suggestions, or [] if absent."""
+    bullets = _bullets(llm.get("cleaning"))
+    if not bullets:
+        return []
+    return [model.Heading(text="Limpieza sugerida", level=2),
+            model.Markdown(text=bullets)]
+
+
+def _pii_block(llm: dict):
+    """DataTable for PII/GDPR findings, or None if absent/empty."""
+    entries = llm.get("pii")
+    if not isinstance(entries, (list, tuple)) or not entries:
+        return None
+    header = ["Columna", "Tipo", "Severidad"]
+    rows = []
+    for e in entries:
+        if not isinstance(e, dict):
+            continue
+        rows.append([
+            _clean_text(e.get("column")) or "—",
+            _clean_text(e.get("kind")),
+            _clean_text(e.get("severity")),
+        ])
+    if not rows:
+        return None
+    return model.DataTable(
+        header=header, rows=rows, title="Datos personales (PII / RGPD)",
+        note="detección automática orientativa — revisar antes de tratar los datos")
+
+
+def build_analisis_llm(profile: dict, ctx: dict):
+    """Build the LLM analysis Chapter, or None if there is no LLM block.
+
+    Consumes ``profile['llm']`` (the block produced by ``eda_llm_insights``,
+    group ``eda``); falls back to ``ctx['llm']``. Returns ``None`` when no LLM
+    block is present or it carries no usable content, so the chapter is omitted
+    rather than rendering an empty section.
+    """
+    profile = profile or {}
+    ctx = ctx or {}
+
+    llm = profile.get(LLM_KEY)
+    if not isinstance(llm, dict):
+        llm = ctx.get(LLM_KEY)
+    if not isinstance(llm, dict) or not llm:
+        return None
+
+    blocks: list = []
+    blocks += _summary_blocks(llm)
+    blocks += _row_meaning_blocks(llm)
+
+    dict_block = _dictionary_block(llm)
+    if dict_block is not None:
+        blocks.append(model.Heading(text="Diccionario de datos", level=2))
+        blocks.append(dict_block)
+
+    blocks += _analyses_blocks(llm)
+    blocks += _cleaning_blocks(llm)
+
+    pii_block = _pii_block(llm)
+    if pii_block is not None:
+        blocks.append(model.Heading(text="Datos personales (PII / RGPD)", level=2))
+        blocks.append(pii_block)
+
+    if not blocks:
+        return None  # LLM block present but every field empty → omit chapter.
+
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)
diff --git a/python/functions/datascience/automatic_eda/chapters_registry.py b/python/functions/datascience/automatic_eda/chapters_registry.py
index 1d6743f4..6dd73237 100644
--- a/python/functions/datascience/automatic_eda/chapters_registry.py
+++ b/python/functions/datascience/automatic_eda/chapters_registry.py
@@ -28,12 +28,12 @@ from . import model
 CHAPTER_ORDER = [
     "portada",       # cover
     "overview",      # df.head + columns/types/nulls/examples + describe
+    "analisis_llm",  # LLM interpretation — sits next to overview (user request)
     "num_distr",     # numeric distributions
     "cat_distr",     # categorical distributions
     "calidad",       # data quality
     "correlacion",   # correlations / associations
     "modelos",       # cheap models (PCA/KMeans/outliers)
-    "analisis_llm",  # LLM interpretation
     "timeseries",    # time-series analysis
     "geospatial",    # geospatial
     "agregacion",    # aggregations / pivots